Comenius University in BratislavaFaculty of Mathematics, Physics

and Informatics

Programe: Cognitive Science

Final report for the project workSchool year: 2014/15

Cheating Students and NaiveTeachers: A Multiagent

Simulation for Social Norms

Author:Simon Knez

Mentor:Martin Takac

Abstract

The aim of the project work was to create a multi-agent simulationfor ethical norms. Ethical norms were used in the scenario of cheatingon exams. The environment was comprised of an teacher and multiplestudents. Each round, the student decided to cheat or not to, whilethe teacher decided to check the student for cheating. The decisionprocess of both agent sets is defined by their norms.In the first section I have written a short introduction to multi-agentsimulations and about the aim of this particular simulation. In thesecond section I have described the properties of agents in the simu-lation. In the third section I have described the simulation itself (theuser interface and how to use the simulation). In the fourth sectionI have described the results of several rounds of simulations. Theserounds differed in the cost function, to show, what kind of environmen-tal properties are most favorable for cheating behavior. I comparedthe norms by the gain score and the grades the cheating students wereable to get. I found, that the most important feature of the norm isthe frequency of the check ups, especially if the student population isa dynamic one i.e. changes their behavior according to it’s experiencewith the teacher. Because check ups are sometimes time costly, work-ing on motivating the students to learning and using different softwaretechnique to minimize the time involved in checking the student forcheating is advised.

Keywords— students, teacher, norm, environment, scenario, simulation,costs, cheating, checking

Contents

1 Introduction 11.1 Norm based multi-agent simulations . . . . . . . . . . . . . . . 11.2 Goals of the project work - minimizing cheating . . . . . . . . 3

2 Methods and tools 52.1 Modeling environment: Netlogo . . . . . . . . . . . . . . . . . 52.2 Simulation design and scenarios . . . . . . . . . . . . . . . . . 6

2.2.1 Main properties of the simulation . . . . . . . . . . . . 62.2.2 Cost functions . . . . . . . . . . . . . . . . . . . . . . . 72.2.3 Student agent type . . . . . . . . . . . . . . . . . . . . 82.2.4 Teacher agent type . . . . . . . . . . . . . . . . . . . . 102.2.5 Environment variables and summary of all the vari-

ables being present in the simulation . . . . . . . . . . 13

3 Implementation 143.1 User interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.2 How to use the software . . . . . . . . . . . . . . . . . . . . . 173.3 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . 19

4 Results 224.1 Norms compared by Gain . . . . . . . . . . . . . . . . . . . . 224.2 Realistic environment . . . . . . . . . . . . . . . . . . . . . . . 324.3 Parasitic behavior . . . . . . . . . . . . . . . . . . . . . . . . . 35

5 Discussion and further development 38

A Full results of norm comparisons 41

B Norms compared by parasitic behavior 45

List of Figures

1 Mr. Bean cheating . . . . . . . . . . . . . . . . . . . . . . . . 43 Netlogo logo . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Netlogo environment . . . . . . . . . . . . . . . . . . . . . . . 64 Cost function matrix . . . . . . . . . . . . . . . . . . . . . . . 85 User interface . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 In world agent set . . . . . . . . . . . . . . . . . . . . . . . . . 177 3D display of the simulation environment . . . . . . . . . . . . 188 Results in R . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 The world model at the end of the simulation in highest learn-

ing costs scenario . . . . . . . . . . . . . . . . . . . . . . . . . 2610 Mixed cheating behavior . . . . . . . . . . . . . . . . . . . . . 3111 Busy teacher world state . . . . . . . . . . . . . . . . . . . . . 3512 Average cheating student grades in gauges . . . . . . . . . . . 37

List of Tables

1 Properties of student agents . . . . . . . . . . . . . . . . . . . 92 Properties of teacher agents . . . . . . . . . . . . . . . . . . . 103 Table of variables . . . . . . . . . . . . . . . . . . . . . . . . . 144 Cheating probability color table . . . . . . . . . . . . . . . . . 175 Gain values for same costs . . . . . . . . . . . . . . . . . . . . 226 Gain values for checking costs being the highest . . . . . . . . 237 Gain values for not checking a cheating student being the highest 248 Gain values for costs for learning being the highest . . . . . . 259 Gain values for getting caught cheating being the highest . . . 2710 Gain values for checking and learning are the highest . . . . . 2811 Gain values for checking and for getting caught cheating being

the highest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2912 Gain values for not checking a cheating student and learning

being the highest . . . . . . . . . . . . . . . . . . . . . . . . . 3013 Gain values for not checking a cheating student and learning

are the highest . . . . . . . . . . . . . . . . . . . . . . . . . . 3214 Average cheating behavior for the teacher’s norms . . . . . . . 36

1 Introduction

1.1 Norm based multi-agent simulations

The goal of norm based multi-agent systems is to create an artificialenvironment (system) populated with software agents, who adopt differentethical principles. Ethical principles or ethics define these main three prop-erties of the agent: duties (responsibilities, obligations), rights (claim rightsfor agents opinion on actions at other agents actions and liberty rights foraction justification) and set of liberties which defines the level of agents au-tonomy [1]. By such, ethics are a moral framework which define the behaviorof agents in a society. The first step in building such an environment is toplan the desirable system dynamic or behavior which emerges from the re-lationship between the contribution of individual agent ethics to large scaleethics of the whole societies. The contribution relation defines:

• how agents interact with each other,

• how ethical dilemmas are dealt between agents,

• how do social norms (norms of groups or societies of agents) emerge.

Norms are a set of principles which define the behavior of societal mem-bers, by empowering coordination and cooperation between them. The aimhere is by having certain expectations from the agent, that the society as awhole will function as it was meant to be. In programming artificial agents,the use of norms reduces the amount of computation and increase the levelof predictability of other agents. To make a society function on the basis ofethical norms, agents have to be able to produce actions based on ethicalnorms (they have to have the possibility to posses ethical norms), to recog-nize them and to have the ability to interact with each other. Agents bythemselfs possess two types of ethical norms [2] :

• prudential norms (p-norms) and

• moral norms (m-norms).

Prudential norms are norms for self discipline and governance which allowagents to maximize utilities in situations. Moral norms on the other hand arenorms based on agents own conscience. In construction a society of artificialagents, those personal or inter-agent norms may interfere with norms of thewhole society or intra-agent norms. Intra-agent norms are divided to: socialnorms (s-norms) and rule norms (r-norms). Social norms are based on the

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mutual belief of agents, where certain expectations are put on the behaviorof single agents, while rule based norms are a set of rules, which agents agreeupon to be following.

There are two main types of implementations of ethical norms into aagent based system. The first is a by using central control and the second isby using an open system. With central control we decrease the adaptabilityof the system and put the task of norm creation into the developers hands(top-down approach [3],[4]). In the case of the open systems, the societyof agents evolve their own ethical norms, based on learning. This type ofapproach ensures a high level of adaptability (bottom-up approach [5]). Thequestion of centralized or open system is important for the question of how areconventions (templates structuring agents actions) formed. As I mentionedbefore, the centralized approach puts the emergence of norms into the handsof the developer. This means that agents possess hardwired norms, whichdon’t change through time. In the open system we can make emergencepossible for inter-agent as well as intra-agent norms. Inter-agent norms canevolve using different learning and strategy update functions [6, p.215]:

• highest cumulative reward (based on agents assessment),

• agent communication or memory exchange between agents,

• agents communication on success,

• agent memory restart, which allows openness to new ideas,

• ....

For intra-agent norms to evolve, there have to be certain types of mechanismswhich will make agents adhere to them. Those mechanisms may be [2]

• authority,

• rational appeal,

• emotions,

• crowd following,

• ....

All of this mechanisms and learning strategies are implemented based on thedesired complexity of the multi agent system.

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Norm life-cycle According to the paper of Savarimuthu and Cranefield [2]a norms life cycle includes three parts:

• norm formation,

• norm propagation and

• norm emergence.

These parts of the cycle can be implemented in several ways. Norm forma-tion is the first part of the cycle, where norms are identified by agents. Normidentification can be based on game theory, where we use machine learning,data-mining or simple imitation techniques to maximize agents utilities. Adifferent approach of implementing norm identification is by using a cognitiveapproach. A cognitive approach uses interactions and observations, based onwhich the agent identify norms. Second part of the norm life-cycle is thenorm propagation, which includes norm spreading (rate of norm distributionthrough the society) with mechanisms like entrepreneurship, leaderships, cul-ture and evolution and norm enforcement were the purpose is to sustain theestablished norms. The later can be done by using rewards and sanctions onagents. The last part of the cycle is the norm emergence, where local agentviews reach the magnitude of global agent views or views of the majority ofthe society.

1.2 Goals of the project work - minimizing cheating

In a short article from 1993, professor Stephen F. Davis wrote about theproblem of dishonesty (in high schools and colleges) in those times [7]. Heespecially noticed that cheating is a repeating act i.e. students who cheat, doso repeatedly and continue doing from high school to later in college (99% ofmultiple offenders in college were also cheating on multiple occasions throughhigh school). Also the author found that cheating behavior has a link to thepressure to get good grades (69% of students reported that grade pressurewas the major reasons for cheating) and the group behavior (cheating beinga normal part of our lives). Lastly the author found that cheating takes placein a waste range of creativity such as:

• hand and feet position system,

• using corners of desks as markers for answers (choice fields),

• answer paper trading,

• stealing the copy of the test and memorizing the answers,

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Figure 1: Cheating on exams is a well known phenomenon in todays schools. Thatwas demonstrated also by Mr. Bean.

• writings on the arms, etc. .

Through this project work the main idea was to create a multi-agentsystem for social norms. The agents and their norms would be put in a schoolsystem like environment, where two agent types would interact with eachother. The interaction would be of such a type, that one agent - the studentdecides to cheat or not on the exam, while the other agent - the teacherdecides to check the particular student for cheating or not. By this I wantedto simulate different school environments (which have different values such assanctions, difficulty of exams etc.). In this environment agents with differentnorms would decide if they want to cheat (students) and if they want to checkfor cheating (the teacher). By this I would get the particular environmentalproperties and norms which produced certain results (percentage of cheatingstudents, grades of cheating students, hours spent checking for cheating etc.).With this knowledge I can then propose how can the issue of cheating anddishonesty be approached.

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2 Methods and tools

2.1 Modeling environment: Netlogo

For the purpose of implementing a multi-agent system, I have used Netlogo, amulti-agent environment, for simulating different kinds of phenomena. It wasauthored by Uri Wilensky in 1999. Netlogo is available for free on the sitehttps://ccl.northwestern.edu/netlogo/ and can be run on Windows,Linux and Mac OS. Its logo is shown in the figure 3.Netlogo enables the user to module different kinds of environments, pop-ulated with artificial agents, which behave based on rules the programmerapplies to them. By this, we can observer agent behavior on micro and macrolevel. When programming in netlogo we use the world and the agents. Theworld is the whole environment in which agents interact i.e. it is the set ofall the agents present. There exist two types of agents:

• turtles and

• patches.

Turtles are movable objects while patches are immovable objects over whichturtles move. Turtles can also be breed which is the same as creating newclass instances. Patches and turtles have different properties, which can bealtered only through the agent itself (values of the properties can be changedonly from within the agent). The netlogo environment is comprised of 4 parts.The object menu on the top, which is used to create the user interface andcontrol the speed of the simulation, blank space where we can put differentuser interface objects, the world model, which displays the agents (patchesand turtles if any) and the console window which enables us to ask aboutthe state of the world (properties of agents). The world model does not onlydisplay agents, but it also allows to click on them and display their properties.Picture 2 displays an agent, which was clicked on, to display it’s properties.

Figure 3: This is the logo of Netlogo

1By default, the shape of turtles is uneven pyramid like shape.

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Figure 2: The picture displays the whole netlogo environment. On the top, there isa menu for adding new objects for the user interface. Beneath on the left side, thereis blank space for these objects, on the right side there is the world monitor. Theworld in the picture is comprised of multiple patches (in this particular picture, twopatches were colored red and green so they can be distinguished) and one (green)turtle1. If we click on the turtle we can see it’s properties. The same can be donewith every patch.Bellow the world and the blank space for the user interface objects,there is the command console.

2.2 Simulation design and scenarios

2.2.1 Main properties of the simulation

The simulation is composed of two basic agents:

• teacher and

• student.

Through the simulation there is only one teacher and multiple students.These agents are placed in a environment of test examination. Every round,

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each student decides to cheat or not to cheat i.e. to learn for exam. Onthe other side, for every student, the teacher decides if he wants to checkthe particular student for cheating or not. Agents make decision based onparticular norms, which are hard-wired into them. Every norm does, basedon a particular equation, provide the probability to cheat (for student) orcheck (for teacher). Every decision of an agent is at the end of the turn,graded based on a cost function. The properties of environment are set withthe cost functions for both agent types. The cost functions define what arethe costs2 for a particular decision in combination with a particular outcome.Agents decision process can be linked with the cost function, but this is notobligatory and is implemented only in particular norms. The purpose of thesimulation is to display and compare how different norms of agents effect thetotal costs collected throughout the simulation.

2.2.2 Cost functions

Four cost values are implemented in the simulation, two for each agent type(student and teacher). The cost values for the teacher are:

• cost of checking - additional hours spent and

• cost of not checking cheater - consequences of letting a cheater cheat.

The cost values for student are:

• cost of learning - hours spent for learning and

• cost of being caught cheating - consequences of getting caught cheating.

These four costs can be set through the simulation interface and can influ-ence the dynamic of decisions by each agent if the norm which the agentuses, is cost based3. The value of the costs range from 1 to 10. The costfunction matrix is shown in the picture 4. For a more in world applicable in-formation, the agents store some additional statistics besides collected costs.These statistics or properties are listed in the following two sections wheredescription of two basic agent types is written.

2Costs have a negative connotation.3More about cost based norm for teacher agent in section 2.2.4 and student agent in

section 2.2.3

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Figure 4: The matrix shows, what are the costs of a particular decision of bothagents (teacher and student). Each square displays what costs would be in effect ata particular decision. In each square, the upper cost is applied to the teacher andthe lower one to the student. The blue colored costs are marked as good outcomesand the red colored costs are marked as bad outcomes, based on our intuition.This however can be changed through the user interface i.e. that the good and badoutcomes are effectively a consequence of costs set through the user interface.

2.2.3 Student agent type

Students are agents, which decide to cheat or not to cheat and get gradedbased on the decision of the teacher to check or not. In basic terms, studentscollect costs, but for a more natural way of showing the consequences of theirdecisions, they possess additional properties. Label 1 shows those propertiesand their relation with the costs4.

4not all properties are related to the costs. Some of them serve as a way to calculatedifferent probabilities

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PROPERTY RELATIONS EXPLANATIONhours costs of learning student spends time learn-

ingreputation costs of being caught

cheatingstudent gets sanctioned ifcaught cheating

checks probability of gettingchecked

student counts the numberof times he got checked forcheating

grade none average grade of the stu-dent5

Table 1: Three properties are essential for student agents. Two of them are relatedto costs (marked as red). The checks property is used for calculating the probabilityof getting checked, while the grade is used for calculating the average grade thestudent got.

Behavior of a student can be either static or dynamic. Static studentshave a set rule, which means that their norm, which defines the probabilityof cheating, does not change throughout the simulation. In the simulation,static students are divided into two groups.

• Non cheaters - probability of cheating is 0% and

• Cheaters - probability of cheating is 100%.

On the other hand, dynamic students change their probability of cheatingbased on implemented norms. Until this point, dynamic student can pos-sess only a cost based rule, which implies that the student decides to cheat,based on the costs (described in the previous paragraph), that are definedand set and the beginning of the simulation. The decision process for the dy-namic student with cost based norm is executed on the basis of the followingalgorithm:

if SCH6 > (SPC7 * SCCC8)

then:

CHEAT

else:

DON’T CHEAT

5Average grade range is from 0 to 1.6SCH - student’s cost for honesty.7SPC - student’s probability of being checked.8SCCC - student’s cost of being caught cheating.

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Because the cheating decision is a continuous variable from 0 - 100 the abovealgorithm is transformed in the way, the value of cheating probability is afunction of costs and getting checked probability. The implemented functionto determine the probability of cheating is as follows:

d = SCH - SPC * SCCC

SCP9 = 1 / (1 + e^(-d))

2.2.4 Teacher agent type

Through the simulation only one teacher is present in the environment. Theteacher has a set norm, based on which he decides to check a student forcheating or not. The norm doesn’t change through the simulation. Theteacher has besides the collected costs additional properties, which are relatedto the costs or are needed for calculation of other valuable information. Theseproperties are shown in table 2.

PROPERTY RELATIONS EXPLANATIONhours costs of checking teacher spends time check-

ingexperience cost of not checking a

cheating studentteacher tracks the numberof cought cheaters

revisions probability of a stu-dent cheating

teacher counts the numberof times he interacted withstudents

Table 2: Three properties are essential for teacher agents. Two of them are relatedto costs (marked as red) the last one only counts the number of interactions withthe students. Based on hours and experience, we can calculate the accuracy ofspent hours for checking.

Till this stage of development, the teacher agent has the ability to possessone of the following norms:

• default rule only,

• student’s reputation,

• teacher’s experience,

9SCP - student’s cheating probability.

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• student’s reputation and teacher’s experience,

• limiting teacher’s hours and

• cost based.

Default rule only The default rule only sets the percentage of checkingprobability of the teacher, which ranges from 0 to 100%. This value remainsstatic through the whole simulation and tells the percentage of students thatthe teacher will check each round. The default rule can be used in combina-tion with other norms, if those norms have insufficient number of argumentsto make a decision.

Student’s reputation The student’s reputation norm is using the repu-tation of each student, to decide, if the teacher checks for cheating or not.This is implemented on a basis of the following algorithm:

STUDENT’S REPUTATION:

if SNC == 0 10

then:

TCP = DEFAULT RULE 11

else:

TCP = SNCC12 / SNC

The student’s reputation is a highly effective norm for static students (stu-dents which don’t change their norm through the simulation), since theteacher manages to effectively find the behavior property of a static stu-dent. Depending on the default rule, the number of round varies, until theteacher gets a big enough number of checks for each student and thereforeenough information if the student is a cheater or not.

Teacher’s experience Teacher’s experience is using teacher’s own previ-ous experience to decide to check or not. By previous experience is meantthe percentage of cheaters the teacher ”thinks” are cheating. This norm doesnot take in consideration the reputation of a particular student, which mightleave opportunity for students to cheat without being noticed. Similar asthe student’s reputation norm, this norm also uses the default rule, but onlyinitially, where the teacher doesn’t have any checking history. The norm isimplemented by the following algorithm:

10SNC - Number of times the student got checked.11TCP - Teacher checking probability12SNCC - Number of times the student got caught cheating.

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TEACHER EXPERIENCE:

if TNE == 0 13

then:

TCP = DEFAULT RULE

else:

TCP =( W14 * (TNCC15 / TNC16) ) + (1 - W) * DEFAULT RULE

Student’s reputation and teacher’s experience This norm combinesprevious two norms17. Each of the norms is valued equally i.e. CP value ofboth norms are added and divided by 2. In certain circumstances only oneof both norms is used. The next algorithm shows the implementation of thisnorm and shows that if the teacher does not have any experience yet, he usesonly the student’s reputation norm:

TEACHER’S EXPERIENCE AND STUDENT’S REPUTATION:

if TNE

then:

TCP = DEFAULT RULE

else if SNC == 0 :

TCP = TEACHER’S EXPERIENCE

else:

TCP = (TEACHER’S EXPERIENCE + STUDENT’S REPUTATION) * 0.5

Limiting teacher’s hours Limiting teacher’s hours is a norm, whichchanges the norm usage based on the maximum set hours, the teacher shouldon average spent on a student.18The norm is constructed in the way, thatuntil this limit of spent hours checking is not reached19, the teacher usesthe student’s reputation norm, otherwise he doesn’t check for cheating. Thealgorithm for this norm is implemented as follows:

LIMIT TEACHER’S HOURS:

if ATH < 0.3 20

13TNE - Teacher has no experience i.e. has not yet checked for cheating14W = 1 - eˆ(-0.01 * TNC)15TNCC - Number of times the teacher caught a student cheating.16TNC - number of times the teacher checked for cheating.17Student’s reputation on page 11 and Teacher’s experience on page 1118Every time the teacher checks for cheating, he spends and additional hour, which

through the cost function is defined as ”costs for checking”.19until this stage of development, the limit is set to 0.3 i.e. additional 33% of time is

spent on checking for cheating.20ATH - average hours spent on each student for cheat-

ing, where 0 means that teacher newer checks and 1 that the teacher always checks.

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then:

TCP = STUDENT’S REPUTATION

else:

TCP = 0

Cost based Cost based norm is using cost function to determine the prob-ability of checking. Teacher’s decision incorporates the probability of eachpossible outcome and weighted with the values of the costs. If the costs forchecking are low in comparison to the costs of not checking cheater, then hewill mostly check, if not he’ll do otherwise. Because the cost based rule needsthe probability of the student cheating, it incorporates previously mentionednorms (except Limit teacher hours). The algorithm for cost based functionis as follows:

d = TCC21 - SCP22 * TCNCC23

CP = 1 / (1 + e^(-d))

2.2.5 Environment variables and summary of all the variables be-ing present in the simulation

Besides the variables belonging to different agents, I have used additionalglobal variables, to measure the effects of the interaction on the whole envi-ronment. This variables are:

• actual cheating - number of times cheating behavior has occurred throughthe simulation and

• gain - subtraction between teacher’s accuracy of used hours and theavg grade of cheating student.

For a better organization of all variables that are used during simulation andlater in the statistical analysis, the following table (table 3) shows all thevariables used in the simulation (used in monitors, file storage and statisticalanalysis), which agent set they belong to and what are the minimum andmaximum (if any) values they can hold.

21TCC - teacher’s costs for checking.22SCP - student’s cheating probability, that is determined based on a additional norm.23TCNCC - teacher’s costs for not checking cheating student.

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VARIABLE AGENT SET VALUESStudent id Student min = 0 max = NRS25

Static Student True or FalseCheating probability Student min = 0 max = 100Cheated Student True or False

Student’s costs Studentmin = 0max = NR26 * max(SCH,SCCC)

Average student’s costs Studentmin = 0max = max(SCH, SCCC)

Student’s grade Student min = 0 max = NRAverage student’s grade Student min = 0 max = 1Student hours Student min = 0 max = NRTeacher id Teacher 0

Teacher’s costs Teachermin = 0max = NR*max(TCC,TCNCC)

Teacher’s hours Teacher min = 0 max = NR*NRSTeacher’s experience Teacher min = 0 max = NR*NRSAccuracy of spent hours Teacher min = 0 max = 100Checking probability Teacher min = 0 max = 1Cheating % Teacher min = 0 max = 100Actual cheating % Environment min = 0 max = 100Gain Environment min = -1 max = 1

Table 3: The table shows all variables that are used during the simulation eithershown in the user interface or saved to the file and later used in the statisticalanalysis. The variables are described as to which agent set they belong and whattheir minimum and maximum values are.

3 Implementation

3.1 User interface

The user interface is a big part of the simulation. Besides showing numericalresults it displays the behavior of the agents through the simulation, whichis also equipped with some data visualization. The user interface is dividedinto three parts:

• simulation controls or inputs to set initial values and simulation envi-ronment properties,

25NRS - number of students26NR - number of rounds passed in the simulation.

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• monitoring values of costs and other properties for agent types to showand compare results of the simulation,

• world monitor to display the behavior of agents through the simulationand to visualize agents properties as average grade, collected costs andstudent’s cheating probability.

Picture 5 shows the user interface with all the above mentioned parts.

Figure 5: The user interface for the simulation is divided into three parts. Thesimulation controls, the monitoring of the various agent variables and the worldmonitor.

The simulation controls enable the user to control the simulation andset the simulations environment. First three objects are simulation flowbuttons, which enable to setup the simulation, clear the settings and run thesimulation. Bellow the buttons there is a two options chooser, which enablesthe user to choose if he wants to save the states of agents (and their properties

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defined in previous chapters) into a file. The data in the file are separated bya semicolon. The next slider is the nr Ticks slider, which sets the number ofrounds the simulation will run over27. Next are several sliders which set thenumber of students (from 0 to 100), percentage of static students (0 to 100),distribution of cheating static students(0 to 100), costs and the default rulevalue (default checking percentage). The remainder of this part of the userinterface is comprised of choosers which define the norms agents use throughthe simulation.

Monitors Monitors report values of desired agent properties. Monitorscan display also a calculation on different properties. First three monitors(which are labeled above with ”Students statistic”) display the costs col-lected by students. Students are grouped into cheating students28, goodstudents29 and dynamic students. The next section of monitors (labeled”Teacher statistic”) shows the costs and additional statistics for cheatingand non-cheating dynamic students. The last part of monitors displays thevalue of teacher properties and of the whole environment. This section showsthe teacher’s experienced cheating percentage and actual percentage, the ac-curacy of checking usage, collected costs and the so called gain. Gain iscalculated as a subtraction between the accuracy of spent hours for checkingand the average grade the cheating students get30. Next to the monitors forthe dynamic students, there is a plotting object - plot. This plot displays andcompares how the average grade of the students (students are divided intostatic cheaters, static non-cheaters and dynamic students) changes throughthe simulation.

World monitor World monitor displays the agents in the world and theirbehavior. As it can be seen on the right side of the picture 5 on page 15,the simulation world is comprised of agents with a human figure like shapesurrounded by colored squares and an agent above them, which is not sur-rounded by a square. This particular agent is the teacher, the others are thestudents. A closer look of the teacher and all three types of students (dy-namic, static cheating and static non-cheating) is shown in picture 6. Agentsbodies are colored based on their cheating probability, while the surroundingrepresents the color of the initial property of the agent. Through the simula-tion, the surrounding color gets brighter or darker. A brighter color presents

27Each round, each student interacts with the teacher28Average cheating percentage is lower than 50%29Average cheating percentage is greater than 50%30Cheating students being the students which cheating percentage is greater than 50%31The teacher does not have a surrounding color as other agents.

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Figure 6: All three different shapes of agents. From left to right: static cheatingstudent, static non-cheating student,dynamic student and the teacher.31

a larger cost accumulation. Students surrounded by the darkest color tonesare therefore the most successful.

COLOR SCP VALUEred shades SCP >80%orange shades 80% <SCP >60%yellow shades 60% <SCP >40%blue shade 40% <SCP >20%green shades SCP <20%

Table 4: The table shows the color shades of the student’s body that correspond tothe cheating probability of the student.

Every round each student faces the teacher and decides to cheat or not to.After the decision, the student changes his color, which shows his cheatingprobability including the decision in this round (table 4). After the studentdecides, the teacher decides to check or not to. This is then the final step ofthe decision process. After the decision, costs are being calculated accord-ing to the cost matrix. At the end of the round, the surrounding squaresget darker or brighter. Also above every student, his average grade is dis-played. Netlogo has also an option to display the world in a 3D environment.Picture 7 displays how the teacher student world looks in 3D.

3.2 How to use the software

To run the simulation, the user has to first set the environment properties,through the set of inputs.32 When the environment setup is set as desired,the user has to click the setup button, to create the desired environment.How the environment looks like is graphically displayed on the world mon-itor. If the user wants to change the environment, he needs to change thedesired properties of it and click setup again (he can also click the clear but-ton first, which clears the whole world monitor). Also, before running the

32Inputs are described in the previous section.

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Figure 7: Netlogo also supports a 3D display of the world environment.

simulation (between environment setup) the user has to select if the resultsof the simulation33 are stored into a file. As mentioned in previous chapter,the simulation variables are stored as semicolon separated values. Thereforethe user should save the data into a .csv or .txt file format. For a bettertransparency of the different sets of results, before every new simulation, theuser should create a new file to store the results of the simulation. The olddata will not be lost, but the new data will be appended. It is however possi-ble to store all the data into one file and still separate them by the followingequation:34.

Number_of_Rounds35 * Number_of_students36

The file output can be later used in different statistical software (excel, spss,R, etc.). The picture 8 shows a summary of the data, collected after asimulation with 240 rounds. Through the simulation, the user can change thespeed of the simulation by changing the slider above the user interface. Thisslider is a part of the netlogo environment. After the simulation has stopped

33every interaction of a particular student with the teacher34The equation determines the number of rows that belong to a particular simulation35Through the user interface, this value is set through the slider nr Ticks.36Through the user interface, this value is set through the slider Nr students.

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Figure 8: This display the output for summary command in R. To make this anal-ysis possible, the user has to import the file via the read command with attributes ofHead=TRUE and sep=“;”. After the import, the command summary can displaysuch and analysis of the data. Note that the data displayed on the above picture donot accurately present the statistics for the simulation. The file contains values forvariables after every interaction and therefore this skews the statistic of the endresults. Also variables such as hours, costs etc. are not calculated as an average,but represent the sum of them till the particular point of the simulation.

running, the user can use the monitors to view the results of the simulation,observe how the grades of students have changed through the simulation andobserve how the world monitor looks like (with different colored agents andshaded squares around them). If the user has chosen to write the values ofevery interaction of agents to a file, he can analyze the data on a more deeperlevel.

3.3 Statistical analysis

Three main distinct comparisons between norms have been done. In the firstgroup I have compared norms of students and teachers while adjusting costs.The student population was the same for each simulation which is comprised

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of 80% dynamic students, 10% static cheating students and 10% static non-cheating students. Number of students is always at 100 and number of roundsis always set to 240. I ran the simulation through 9 different scenarios:

• costs are all the same,37

• costs for checking are the highest, 38

• costs for not checking a cheating student is the highest,

• costs for learning are the highest,

• costs for getting caught cheating are the highest,

• costs for checking and costs for learning are the highest,

• costs for checking and costs for getting caught cheating are the highest,

• costs for not checking a cheating student and costs for learning are thehighest and

• costs for not checking a cheating student and costs for getting caughtcheating are the highest.

For every scenario each norm was described by its score or value of the gainvariable. The worst and the best norm (lowest and highest gain score) will bedescribed in a more detailed fashion to explain the results. This results willform the basis for the analysis of parasitic behavior (where does it occur themost frequent). Note that the costs in this scenarios take only two values,1 or 10. Because the cost based decision is calculated on the basis of the dvalue, defined as39:

d = SCH - SPC * SCCC

there is a difference of behavior if costs are the same, but of different valuesthan in a previous scenario. For instance, the d value would be 2,5 in thecase of SCH and SCCC values being 5 and the SCP being 0,5 while it willhave a value of 0,5 in the scenario, where SCH and SCCC values are 1. Sowhile both costs are the same values, the importance of the decision is fairlyweak. Initially, where the student does not have any experience of beingchecked his SCP value is 0. By this, in the scenario where both student costs

37initial value of costs is set to 138highest costs are set to 1039The whole algorithm for cost based decision can be seen on page 10

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have a value of 5, the cheating probability is 99,3% while in the scenario ofcosts having a value of 1, the cheating probability is 73,1%. This changes thedynamic of students’ decisions and therefore the effects of particular teacher’snorms. As mentioned before, in this first part of norm comparisons, valuesof 1 and 10 are used as values for the costs. This is done because, I wantedto exaggerate the difference of cost values as much as possible.In the second group I have set up different “realistic” environments, and thencompared them based on the gain score, teacher’s properties and students’properties. This and the next group of norm comparisons will serve as thedata for my conclusions. In the last group, I will set up several environmentswhere parasitic behavior should be experienced. By parasitic behavior Imean, that the cheating students should get good grades, because of differentenvironmental properties. The purpose of this group of results is to see whatkind of scenarios are at most prone to parasitic behavior.The results of the simulation are measured with the monitor objects describedon page 16, but the output file could be used as well.

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4 Results

4.1 Norms compared by Gain

Costs are all the same The environment settings where costs were allthe same resulted in a relative low gain score (results and average of normsare shown in table 5). Which means that even the best norm is still onlyslightly better than the default rule only with 0% probability of checking i.e.teacher newer checks for cheating. The main reason for this is that becausethe costs for students are the same, there is no difference or benefit to chooseeither of the decisions (cheat or not to). At the end students’ decisions leanslightly towards cheating (50,1%). The most successful norm in this scenariowas the default rule with 100% checking percentage, while the worst one wasthe cost based norm. While the default rule at least checks 50% of cheatersother norms fail to do so. Therefore the grades for cheating students areat least around or slightly bellow 0,5. On the other side, since the teacherchecks for every student the efficiency of used hours falls to 50%. All othernorms are better in this respect, but still the difference in values of thosevariables is not as big, so the difference in gain is also not as outstanding.The collected data for this and all the other scenarios described on the nextfew pages, are shown in the first three pages of the Attachments section onpage 41.

TEACHER’S NORM GAIN VALUE100% default 0,07Student’s reputation 0,05Reputation and experience 0,04Default 0% 0Teacher’s experience -0,03Limit teacher’s hours -0,04Default 50% -0,06Cost based -0,07

AVERAGE -0,01

Table 5: The table shows the norms sorted by their gain value at the end of thesimulation. All simulations demonstrate a low gain value in this particular sce-nario.

Costs for checking are the highest In this environment, all the normshad the same value (of 1) except the cost for checking, which was set to 10.

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This means that the teacher’s cost to check the student for cheating is sub-stantially higher than any other costs and should therefore be expected thatchecking should be done only in rare or important circumstances. Becausethe value of costs influence only cost based norms, we shall therefore expectthat the other non-cost based norms, will not be affected by the costs values.But they would show, what the results would be, if the costs were as such,but would ignore them and rely on a different (none cost based) norm. Asit can be seen in the table 6, the best norm, similar to the previous one,is the default only 100% norm, while the worst one was cost based. Thedefault norm produced the best gain, for the same reasons as in the previouscase, while the cost based norm produced the worst gain results, which wasa lot lower in comparisons to other norms. Here the effect of the high costsfor checking can be seen, since the teacher is reluctant to check for cheat-ing and therefore leads to student behavior that is similar as in the case ofthe teacher’s default only 0% rule. Since the teacher still checked a smallpercentage of students, the gain value is more negative than in the case ofdefault only 0% rule.

TEACHER’S NORM GAIN VALUEDefault 100% 0,07Student rep 0,04Reputation and experience 0,04Default 0% 0Teacher exp -0,04Limit teacher hours -0,04Default 50% -0,05Cost based -0,36

AVERAGE -0,04

Table 6: Table shows the gain values for the scenario of checking costs being thehighest. The effects of high checking costs can be seen in the cost based norm. Thegain value is the lowest, since the cost discourage the teacher to check for cheating.This later also leads to a cheating oriented behavior from the dynamic students.

Costs for not checking a cheating student is the highest In thisscenario, the cost for not checking a cheating student were set at 10 whileothers at 1. This represents a scenario, where letting cheating students get-ting away with cheating is a tremendous negative outcome for the teacher.Similar to the previous case, this cost values, should be noticeably seen inthe cost based teacher norms. The decision dynamic should be similar to the

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default only 100% rule with a slighty less frequency of checking for cheating.The best norm (even though the gain values are still low, because of the lowstudent costs, which encourages cheating behavior), was again the defaultonly 100%. As predicted the cost based norm, produced a similar resultsas the default only 100% rule (gain results are shown on table 7.) Since themajority of students were cheating (80% of the whole population) the defaultrule was a bit better. If there would be more non-cheating static students,the cost based norm should produce a better gain score, since the hours ac-curacy should be higher, while the grades for cheating students should notbe as much different.The norm that produced the smallest gain score was the default only 50%norm. Even though the difference is not as outstanding, the lower accuracyof hours and fairly high grades of cheating students led to the lowest gainscore.

TEACHER’S NORM GAIN VALUEDefault 100% 0,09Cost based 0,08Student rep 0,04Reputation and experience 0,04Default 0% 0Teacher exp -0,03Limit teacher hours -0,04Default 50% -0,05

AVERAGE 0,02

Table 7: The gain scores for norms in the scenario of highest costs for not checkinga cheating student. It can be seen, that the default only 100% norm and the costbased norm produced similar results. Still the average gain score is 0,02 and there-fore this scenario does not represent a environment where cheating is minimized.

Costs for learning are the highest In this scenario, cost of learning isthe highest, which should influence the students, not to learn i.e. try to cheat.The behavior will be influenced by the probability of getting checked (whichis based on previous experience), but still the magnitude of the difference ofboth student costs is as such, that cheating behavior should be present mostof the time.As it can be seen from the table 8 this kind of scenario produces very highscores in some norms. Especially good was the Student reputation (Studentrep) norm which produced a gain score of 0,99 (remember that maximum

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value of gain is 1). The reason for this result is in the nature of this norm,where the teacher keeps track of the number of times he caught the studentcheating. Because the learning costs are so high, the variability of students’behavior is not dynamic to produce a bad prediction by the teacher. The non-dynamic probability of the students’ decisions can be also seen on picture 9,where all of the dynamic students are red (indication of strong cheatingprobability) and are surrounded by darker squares (they did not collect manycosts, since they were not learning). On the other hand, the worst norm wasthe default 0%. In this case, the cheating students are encouraged by thelearning costs and by the un-willingness by the teacher to check, for a cheatingbehavior. Because the teacher doesn’t waste away any hours, the left side ofthe gain equation is 1, but this all is negated because of the good grades ofcheating students.

TEACHER’S NORM GAIN VALUEStudent rep 0,99Reputation and experience 0,92Default 100% 0,9Teacher exp 0,8Cost based 0,41Default 50% 0,4Limit teacher hours 0,33Default 0% 0

AVERAGE 0,59

Table 8: Table presents the gain scores in the scenario where costs for learning arethe highest. The average gain score is fairly high - 0,59 and is especially high withnorms that either check a lot for cheating or use the experience with a particularstudent as a guide.

Costs for getting caught cheating are the highest This scenario triedto induce learning behavior from students, by exaggerating the costs for beingcaught cheating. The average gain value for the whole set of norms is small,with a big difference between the best and the worst norm (results on table 9).The main reason for this, is the low accuracy of the teacher’s hours, which ishighest by using the limit teacher hours norm (54,8%) or student reputationnorm (53,7%). But because this norms produce decision on the basis of aparticular student, if some students do not get marked as a cheater from thebeginning, parasitic behavior can arise. Therefore we can see in both of this

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Figure 9: The picture presents how the dynamic students most of the time producedcheating behavior, to avoid collecting higher costs. Even though their grades werelow (numbers above the students), the costs of learning were to high to changetheir behavior. Therefore the squares around the dynamic students are dark blue,while squares of the static cheating students are dark red. The non-cheating staticstudents are surrounded by bright green colors, which indicates that their costscollected are high.

norms some students adopted a cheating behavior, which is not present atany other norm (except the default 0% norm, which always invokes cheatingbehavior). The best norm, the default only 100%, was the best norm, becauseit eliminated any kind of cheating behavior. This required a big loss inefficiency of teacher’s used hours, but at the end, was still the best way toproduce the highest gain score. On the other hand, the teacher reputationnorm was the worst and held the lowest score by a substantial margin (0,14from the next closest cost based norm). The reasons for such a low gain scorewas the inability to sanction cheating students. While the dynamic studentsdid not cheat most of the time (maximum cheating probability of a studentat the end of the simulation was 29,42%) the static cheating students got

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away with cheating fairly easy, since their score was on average 0,95 (out of1).

TEACHER’S NORM GAIN VALUEDefault 100% 0,11Reputation and experience 0,02Default 0% 0Student rep -0,03Limit teacher hours -0,1Default 50% -0,36Cost based -0,37Teacher exp -0,51

AVERAGE -0,16

Table 9: The average gain value for this scenario was low, but the difference be-tween the best and the worst norm was big (0,62). The best norm was the defaultrule only 100%, because it eliminated parasitic behavior, while the worst was theteacher’s experience, which caused such behavior and still had a low percentage ofefficiency of teacher’s hours.

Costs for checking and costs for learning are the highest In thefollowing 4 scenarios, two costs are being amplified (one for the teacher andone for the students). In all of the cases, the interesting results come, whenboth teacher and students use the cost based norm. In other cases, the resultsare the same as if only the students’ costs were amplified. Because of this,emphasis in the analysis will lie mostly in the case where both agent sets usecost based norms.First scenario amplified the teacher’s costs for checking and student’s costsfor learning. This scenario should invoke cheating behavior, since the costfor learning are high, and this should be amplified if the teacher uses the costbased norm. We can therefore make a prediction that the cost based normand the default only 0% should produce similar results.As it can be seen from the table 10, the average gain value of the norms isfairly high (0,53) and is similar to the gain values of higher learning costs,since most of the teacher’s norms are not cost based. The big difference is inthe cost based norm, which gain value fell from 0,41 to -0,08. This was thelowest gain score in this scenario. It can be seen from the results (last grid inthe second attachment page), that the results of the cost based norm and thedefault only 0% norm are almost identical. As it was predicted, of teacherand student decisions are cost based it amplifies the cheating behavior in a

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similar way as the default only 0% norm does.The best norm was the student reputation norm, which resulted in a gainscore of 1, since it did not allow any parasitic behavior and it produced ahigh (99,95%) of accuracy of teacher’s hours. The main reason, as it was thereason in the costs of learning being the highest scenario (comments on theresults begin on page 24), was that the cheating students were cheating allthe time so their predictability was easy and therefore not a lot of teacher’shours have been wasted.

TEACHER’S NORM GAIN VALUEStudent rep 1Reputation and experience 0,93Default 100% 0,9Teacher exp 0,79Default 50% 0,4Limit teacher hours 0,33Default 0% 0Cost based* -0,08

AVERAGE 0,53

Table 10: The results for the scenario of high costs for checking and learningproduced identical results as the scenario of high costs for learning. The maindifference was when the teacher used the cost based norm which produced a behaviorsimilar to the default only 0% norm. The cost based rule was therefore producedthe lowest gain score, even lower than the default only 0%, while the best norm,similar to the high costs for learning scenario, was the student reputation norm.

Costs for checking and costs for getting caught cheating are thehighest In this scenario, costs for checking and costs for getting caughtcheating were the highest. This should (in the case where both agent setsuse the cost based norm) produce an interesting result in comparison with thescenario, where the checking costs were not as high. In one way the studentsshould favor non-cheating behavior (because of high getting caught cheatingcosts), but through time should change this to cheating behavior, since theteacher will be discouraged to check (because of high checking costs).As it can be seen on table 11, the best norm was the default 100% norm andthe worst norm was the teacher experience norm. It is interesting to observe,that the cost based norm produced a higher gain score as in previous condi-tion where only on of the two costs were amplified (gain score for scenario ofchecking costs being the highest was -0,36 (page 23) and -0,37 in the scenario

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of getting caught cheating being the highest (page 27)). In this scenario thegain score rose to -0,25. Even though cheating and parasitic behavior is stillstrongly present, the efficiency of teacher’s hours was increased.It is interesting to note, that because of costs of getting caught cheating arehigh, in other norms (except the default only 0% and the cost based norm)students decided not to cheat. If however the teacher also decided not tocheck, the students employ new methods for gaining success by eliminatenon-cheating or good behavior, which allowed them to collect a very smallamount of costs (bellow 1).

TEACHER’S NORM GAIN VALUEDefault 100% 0,11Reputation and experience 0,02Default 0% 0Student rep -0,13Limit teacher hours -0,14Cost based -0,25Default 50% -0,35Teacher exp -0,53

AVERAGE -0,16

Table 11: This table shows the gain values of teacher’s norms in the scenario wherecosts for checking and costs for getting caught cheating were the highest. The mainimpact of this scenario can be seen on the cost based norm. It is interesting toobserve, that even though costs for getting caught cheating were high, the studentsstill decided to cheat, since the teacher didn’t because of the high checking costs.

Costs for not checking a cheating student and costs for learning arethe highest In this scenario, two non-synergistic costs were exaggerated.The teacher’s cost for not checking a cheating student and the student costfor learning. While this scenario increased the checking rate of the teacherit should also lure the students into cheating. As mentioned before, in thescenarios where two costs are exaggerated (one for the student and one forthe teacher) the main effect is observed in the situation, where both studentand teacher agents use cost based norms.In this scenario, similar to the scenario of learning costs being the highest (onpage 24) the student reputation produced the best gain score with a score of1, while the worst was the default only 0%. The most important gain scorehowever is the score of the cost based teacher norm, which went from 0,08 incosts for not checking a cheating student being the highest and 0,41 in the

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costs of learning being the highest, to 0,9 in this scenario. The reason forthis is that the student still cheated because of the high learning costs, butthis time, the teacher decided to check more frequently, since the costs of notchecking a cheating student were very high.

TEACHER’S NORM GAIN VALUEStudent rep 1Reputation and experience 0,92Default 100% 0,9Cost based 0,9Teacher exp 0,8Default 50% 0,4Limit teacher hours 0,33Default 0% 0

AVERAGE 0,66

Table 12: The gain score for the norms in the scenario where costs for learning andcost for not checking a cheating student are the highest. The most important gainscore is the score for the cost based teacher norm. It can be seen, that in comparisonto the scenario where only on of the costs was high (either the teacher’s cost or thestudent costs) the gain score has been substantially increased. This scenario haslured the students for cheating behavior, but it also made sure that the teacher waschecking for cheating so no parasitic behavior would be present.

Costs for not checking a cheating student and costs for gettingcaught cheating are the highest In this final scenario, costs for notchecking a cheating student and costs for getting caught cheating were thehighest. This should lead in a highly efficient cost based norm, since theteacher is stimulated to check for cheating, but the students are stimulatednot to cheat because of the high sanctions.The best norm in this scenario was the cost based norm, with a gain scoreof 0,19. This means that the gain score for the cost based teacher normwent from -0,37 in the costs for getting caught cheating being the highest(description on page 25) and 0,08 in the costs for not checking a cheatingstudent being the highest (page 23). The reason for this is that while thestudents were discouraged for cheating, they were discouraged also becausethe teacher was checking more frequently. While this decreased the teacher’shour accuracy it did lower the grades for cheating students. While all of thedynamic students were not cheating (their rate of cheating was at 1,37%)this norm also eliminated any kind of parasitic behavior. The grades for

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Figure 10: This picture shows the world state in the scenario, which resulted inmixed cheating behavior from the dynamic students. Some students almost newercheated (colored green) while others cheated frequently (colored red). The studentsthat were able to cheat while getting away with it, collected the least amount ofcosts, and therefore their surrounding square was the darkest.

static cheating students was at 0. The reason for a lower gain score wastherefore from the lower accuracy of teacher’s hours (18,88%). On the otherhand, the worst norm was the teacher’s experience norm. While the normhad a poor accuracy of teacher’s hours it also lead to the manifestation ofparasitic behavior. The cheating student were checked irregularly, since mostof the other students were not cheating. Some norms (student reputation,limit teacher hours and default rule only 0%) led to non-cheating as well ascheating behavior from the dynamic student. the picture 10 shows the worldmodel, where some students cheat and some do not. This picture presentsthe end state of this scenario with the teacher using the student reputationnorm where 23,75% of the dynamic students were cheating.

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TEACHER’S NORM GAIN VALUECost based 0,19Default 100% 0,11Reputation and experience 0,03Default 0% 0Student rep -0,08Limit teacher hours -0,12Default 50% -0,38Teacher exp -0,51

AVERAGE -0,10

Table 13: This label presents the gain scores for the scenario, where both costs forbeing caught for cheating and not checking a cheater were the highest. It led to asurprising result, where the cost based norm produced the best gain score. Whileall norms led to a lower efficiency of teacher’s hours, the cost based rule at leasteliminated parasitic behavior.

4.2 Realistic environment

In this section I want to show the results of the simulation, if the costs areset to more realistic values i.e. that the cost function represents a cost func-tion which can be observed in real life. We have many types of teachingmethods and norms which are typical for certain teachers or schools, whichin the end leads to different knowledge acquisition in students. Therefore Ihave searched the literature, to find what kind of approaches are there to thecost function and apply them in the simulation. For all simulations that willbe described in through this section, all the environments will have a 90%population of dynamic students, and 5% of cheating and 5% of non-cheatingstatic students. Each simulation will consist of 100 students and last 300rounds. The norms and the costs will be different in each simulation.

Computer examination Exams in a computer software form have re-cently been used in several different examinations. Especially it is used foron-line schools, but the usage is rising in regular schools. The main benefitsare especially the low costs for exam assessment, time efficiency and admin-istration [8]. To convert computer examination into my simulation I proposethe following cost values:

• costs for checking: 1,

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• costs for not checking a cheater: 6,

• costs for learning: 5 and

• costs for getting caught cheating: 8.

The software allows for an time efficient and quick review of the exams. Usu-ally a lot of these softwares have also built in mechanisms to disable cheatingbehavior (limited number of open applications, time constraints for exam,diversity of exams etc.). Because of this mechanisms, the teacher does notneed to spend additional time for checking for cheating. If however cheatingstill occurs, than the examination has to be changed. If cheating occurredoutside the computer environment, the teacher needs to change the physicalsetup of the classroom or prevent the students to have their prohibited notesand texts. On the other hand if the cheating was a result of the software it-self (lack of mechanisms to recognize cheating) than the software needs to bechanged. This may therefore present higher costs for not checking a cheater.For students, the exams are usually easier, since they are mostly constructedin a multi-choice questions forms. The costs for getting caught cheating ishigher, since the software automatically recognizes the cheating behavior anddoes not allow for compromises. Students will decide to cheat based on theircost rules, while the teacher will check based on a default only 70% rule. Thisimplies that the teacher will mostly check students for cheating, but someroom is left open for cheating to occur (as was described above how cheatingcould be realized and what kind of consequence would this bring about.The results show (the spreadsheet for the results is on the 3th page in theattachment section on page 41) that the costs for the teacher is very low (at1,4) and therefore lower accuracy of spent hours (40,83%) doesn’t present aproblem. Static cheating students have low grades (average at 0,3). The dy-namic students were mostly (97% of the whole dynamic student population)deciding not to cheat and had a better grade value than the static cheatingstudents. Three dynamic students were classified as cheating students. Themaximum cheating percentage of a student was at 52% while the minimumwas at 23,15%. Therefore in the computer examination environment, cheat-ing and parasitic behavior is not present frequently and therefore, based onthe simulation results) presents a good way of examining students.

Busy teacher In the scenario of a busy teacher, I set up an environment,where the teacher does not have time to spent for checking the class he isassigned to. Therefore the exams are easier (to allow less time spent for thecorrections since students would be less error prone), and the sanctions forcheating are lower since at the and of the day the teacher does not want

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complicate the situation. The simulation properties for this scenario are asfollows:

• costs for checking: 10,

• costs for not checking a cheater: 2,

• costs for learning: 3 and

• costs for getting caught cheating: 4.

The teacher uses the limit teacher hours norms in combination with thedefault rule only 20%.This setting provided a high level of cheating and parasitic behavior. Whilethe teacher collected some costs (3,93), he also allowed cheating studentsto get a fairly good grade (average at 0,6). The dynamic students wereall demonstrating cheating behavior with an average of 85%. The gradesof the dynamic students had a range between 0,65 to 1 with an average of0,74. On the picture 11 the end state of the world is shown. It can be seenthat some students managed to get an average score of 1 and had collecteda very low values of costs (their surrounding square are the darkest).Theaverage cheating percentage of those students was 95% which implies thatthe most successful students in the busy teacher scenario were the studentswho cheated the most.

The oral exam This final realistic scenario present the situation when stu-dents take exams in a oral fashion. Because the exam takes part individuallythe costs for getting caught cheating are high as are the costs for learningsince the oral exam requires to express the answer immediately. For theteacher, the costs of checking are fairly high, while the costs for not checkinga cheater are very high. The costs values for this scenario is therefore asfollows:

• costs for checking: 7,

• costs for not checking a cheater: 10,

• costs for learning: 7 and

• costs for getting caught cheating: 10.

Since the teacher checks every student for cheating (every student takes theexam under the supervision of the teacher) the teacher in the simulation usesthe default only rule 100%.

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Figure 11: The picture displays the final state of the environment after the simula-tion in the busy teacher scenario. As it can be observed from the picture 8 studentshave a standing out (dark shaded color) square surrounding them. Those studentscollected the least amount of costs and in this circumstance also a perfect gradeaverage of 1. This shows that in the scenario of a busy teacher, parasitic behavioris strongly present.

The results of this final realistic scenario are as such that all of the agentscollected a fair amount of costs. The teacher used a lot of hours to examine allof the students, while the students spent a lot of time learning. No parasiticbehavior was present and all of the dynamic student were expressing non-cheating behavior with an average cheating percentage of 4,9%. Thereforeall of the dynamic students received good grades (average at 0,93).

4.3 Parasitic behavior

In this section I want to show in which situation parasitic behavior takesplace. The goal of this section is to show the environmental settings inwhich parasitic behavior is highly present. This can be done based on thesection 4.1, where I could compare norms on the basis of the gain score. Anegative gain score presents a high parasitic behavior (cheating students havegood grades) with small hours accuracy of the teacher. A gain score howevercan be deceptive, since a good efficiency of teacher’s hours can negate the

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negative effects of the parasitic behavior. Therefore it is best to look at thegrades that the cheating students collected. The norms that allowed cheatingstudents (static or dynamic) to average the best grades, will be analyzed inthis section.If we take a look at the results on the pages 4, 5 and 6 in the Attachmentsection on page 41, we can calculate the average grades of cheating students(students who had cheating percentage above 50%) from all nine simulationscenarios described in section 4.1. In the next table (table 14) the norms areordered (from best to worst40, based on those averages. Picture 12 displaythe same statistics in form of gauges. If we take a look again at the results

TEACHER’S NORM CHEATING BEHAVIORDefault only 100% 0,13Reputation and experience 0,29Student reputation 0,40Teacher experience 0,48Default only 50% 0,55Cost based 0,57Limit teacher hours 0,69Default 0% 1

Table 14: This table shows the average grade of cheating students through all thenine scenarios, where I compared the norms by the gain score. As it can be ob-served, the norm that produced the lowest average grade was the default only 100%followed by the reputation and experience norm. The limit teacher hours and thedefault only 0% were the worst, since the grades from the cheating students werevery high.

on pages 4,5 and 6 in the Attachments section, grades for the norms differbetween the nine scenarios. Still, norms like default only 100% and repu-tation and experience are usually the most effective. By looking strictly atthe grades, the best norms are those, who allow for the most frequent checkups from the teacher. The norms like reputation and experience can be ofgood use, especially if the population of students is well known, but this canlater lead to parasitic behavior, since students get used to not being checked.This can be especially observed with the teacher experience norm, which inalmost half of the cases (4 out of 9) had a grade of above 0,5. Also we cancompare the weak point of the reputation norm by comparing the gradesfrom dynamic and static cheating students. Student reputation norm had

40Best norm is the one where the average grade for cheating students is the lowest

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a allowed cheating students a grade score of above 0,5 in 3 out of 9 cases.And the reason for this was the dynamic behavior of the dynamic students.While the static students were correctly marked as cheating students, thedynamic were a lot less predictable and parasitic behavior was on display.We can observe this in the higher costs for not checking cheater and caughtcheating scenario and in the higher costs for checking and caught cheating.In both cases, the costs for getting caught cheating was high, but if the stu-dent was not caught cheating at the beginning, the parasitic behavior cameon the surface. This might be eliminated, if the additional rule would be setto default only 100% as compared to the default only 50%.

Figure 12: This picture displays the average grades of cheating students, dividedby the teacher’s norms. The green part of the gauge display a good effect of thenorm, the orange part a fair performance and the red part a poor performance.As we can see, the best norm was the default only 100% while the worst was thedefault only 0%.

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5 Discussion and further development

The goal of this project work was to program a simulation which would allowus to test different norms, by which teacher’s decide to check students forcheating on exams. I have created a simulation in the netlogo environment,where the scenario of students taking exams was presented as a game the-ory where the student decides to cheat and the teacher decides to check forcheating. If the student decided to cheat, he could only get a good grade ifthe teacher did not check him. The assumption here was that cheating wouldlead to an advantage. On the other hand if the student decided not to cheat,he always got a good grade, where the assumption was that learning for anexam would lead to a positive exam outcome. The simulation is equippedwith a user interface to set the environmental properties, ability to store theresults into a file and uses the netlogo world model to visualize some of thedata in a form of colors, shades and numbers.The testing of the different norms, was done by running the simulation with9 different definitions or values of the cost function. The values of this costfunctions were constructed in such a way, that one of the teacher’s or stu-dent’s cost was 10 times greater than the other (the cost function is describedin chapter 2.2.2 on page 7). The assessment of the norms was based on thegain score, while the collected costs displayed what amount of costs woulda particular behavior lead to. After the initial comparison of norms basedon the gain score, I tried to demonstrate how this cost function might bemapped to some realistic environments or situations and found that scenar-ios like oral exams and software based exams are very strong at eliminatingcheating behavior. In the last section I compared the averages of the gradesthat cheating students collected through different simulations. By this, Itried to show which norm was the most successful at down-grading cheatingstudents. I found that the norms that provided the most frequent check ups,like the default only 100% norm was by far the most successful. The maintake-home message from this research is that cheating behavior can be verydynamic and so relying on a set principle or norm will sometimes lead to anundesired outcome. Therefore the aim of every teacher should be to lowerthe costs of checking for exams, while also demonstrating the importance oflearning for the students, so that their costs for learning will be low. Theformer can be achieved with using technology (software programs) for de-tecting cheating, while the later can be achieved by a better explanation ofthe discussed topic in the class and by regular homeworks, which promotelearning in a more distributed manner. This later leads to easier learningand understanding, which will discourage the students to cheat. Also havingsome strict honor code or sanctions which are described in the beginning of

38

the class might be helpful as well. newline The simulation itself, might needsome additional updating. Especially of my interest would be the ability toadd additional norms for the student (at this stage of development only costbased norm is used), multiple teachers being present at the examination,add different forms of cheating and the difficulty to cheat (which would bea property of the teacher). It would be also good to implement a kind of agroup dynamic, where groups of students would spread their personal successwith a particular decision to other students. By writing all this additionalfeatures, I can see that further development of the simulation will be a lotof fun and will also lead to a better realistic re-enactment of the real humanenvironment in situation of cheating students and naive teachers.

39

References

[1] A. Ruvinsky, “Computational ethics,” Encyclopedia of InformationEthics and Security, pp. 76–88, 2008.

[2] B. T. R. Savarimuthu and S. Cranefield, “Norm creation, spreading andemergence: A survey of simulation models of norms in multi-agent sys-tems,” Multiagent and Grid Systems – An International Journal, vol. 7,pp. 21–54, 2011.

[3] Y. Shoham and M. Tennenholtz, “On social laws for artificial agent soci-eties: off-line design,” Artificial Inteligence, vol. 73, pp. 231–252, 1995.

[4] D. Hales, “Group reputation supports beneficent norms,” Journal ofArtificial Societies and Social Simulation, vol. 5, no. 4, 2002. Online;http://jasss.soc.surrey.ac.uk/5/4/4.html.

[5] R. C. Giulia Andrighetto and P. Turrini, “Emergence in the loop: Simu-lating the two way dynamics of norm innovation,” 2007.

[6] M. Wooldridge, An Introduction to Multi Agent Systems. 2002.

[7] S. F. Davis, “Academic dishonesty in the 1990,” The public perspective,pp. 26–28, September/October 1993.

[8] A. Russo, “Mixing technology an testing: Computer-based assessmentslend flexibility, quick turnaround and lower costs, supporters say,” SchoolAdministrator, vol. 59, pp. 6–12, April 2002.

40

A Full results of norm comparisons

The following three pages display the full results of all the nine comparisons(first nine tables) described in section 4.1 and results of the realistic scenarios(last table) described in section 4.2. The first two columns present the prop-erties of the teacher agent (the average costs per student and the accuracy ofused hours). The next two columns show the results for the static cheatingstudents (the average of collected costs of students and their grade averages).The non-cheating static students were excluded in the table (lack of space).Their costs and grades are always static.41 The next three columns displaythe results for all the dynamic students. Besides the cost and grade average,the average cheating probability of the dynamic students is displayed as well.The next six columns are divided into two groups - dynamic cheating andnon-cheating students. The first column displayed the number of studentsbelonging to each group, while the next two columns display the average costand grade values. The last column displays the gain score, which is a calcu-lation of the success of the particular norm on the environmental scale i.e. ittakes into account the accuracy of teacher’s spent hours and the amount ofparasitic behavior.42

41Grade = 1, Costs = SCH42Parasitic behavior is defined as the average grade of cheating students. The bigger

the grade, the bigger the parasitic behavior.

41

Rule Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Default 0% 0,69 - 0 1 0,26 (0,19 - 0,33) 1 73,11% 80 0,26 (0,19 - 0,33) 1 0 - - 0Default 50% 0,75 60,16% 0,5 (0,45 - 0,54) 0,5 (0,46 - 0,55) 0,68 (0,59 - 0,77) 0,69 (0,62 - 0,75) 62,28% (60,47% - 64,47%) 80 0,68 (0,59 - 0,77) 0,67 (0,62 - 0,75) 0 - - -0,06Default 100% 0,9 50,76% 1 0 1 0,49 (0,41 - 0,57) 50,10% 80 1 0,44 (0,41 - 0,57) 0 - - 0,07Student rep* 0,65 69,10% 1 (0,99 - 1) 0 (0 - 0,01) 0,64 (0,21 - 0,84) 0,73 (0,55 - 1) 63,15% (58,05% - 73,02%) 80 0,64 (0,21 - 0,84) 0,65 (0,55 - 1) 0 - - 0,05Teacher exp* 0,77 58,23% 0,59 (0,54 - 0,66) 0,41 (0,34 - 0,46) 0,75 (0,69 - 0,82) 0,64 (0,58 - 0,7) 60,04% (58,16% - 62,25%) 80 0,75 (0,69 - 0,82) 0,62 (0,58 - 0,7) 0 - - -0,03Reputation and experienc 0,73 61,97% 0,8 (0,75 - 0,84) 0,2 (0,16 - 0,25) 0,77 (0,7 - 0,84) 0,63 (0,54 - 0,71) 59,45% (57,34% - 61,66%) 80 0,77 (0,7 - 0,84) 0,58 (0,54 - 0,71) 0 - - 0,04Limit teacher hours 0,65 72,15% 0,67 (0,63 - 0,71) 0,33 (0,29 - 0,37) 0,52 (0,2 - 0,77) 0,81 (0,63 - 1) 66,88% (61,46% - 73,02%) 80 0,52 (0,2 - 0,77) 0,76 (0,63 - 1) 0 - - -0,04Cost based* 0,72 63,71% 0,44 (0,41 - 0,49) 0,56 (0,51 - 0,59) 0,62 (0,56 - 0,7) 0,73 (0,68 - 0,78) 64,24% (62,64% - 65,51%) 80 0,62 (0,56 - 0,7) 0,71 (0,68 - 0,78) 0 - - -0,07

Rule Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Default 0% 0,69 - 0 1 0,26 (0,2 - 0,35) 1 73,11% 80 0,26 (0,2 - 0,35) 1 0 - - 0Default 50% 4,82 60,90% 0,5 (0,43 - 0,55) 0,5 (0,45 - 0,57) 0,69 (0,62 - 0,76) 0,68 (0,61 - 0,74) 62,21% (60,77% - 63,61%) 80 0,69 (0,62 - 0,76) 0,66 (0,61 - 0,74) 0 - - -0,05Default 100% 9 50,83% 1 0 1 0,49 (0,41 - 0,55) 50,10% 80 1 0,44 (0,41 - 0,55) 0 - - 0,07Student rep* 3,94 68,57% 1 (0,99 - 1) 0 (0 - 0,01) 0,64 (0,24 - 0,83) 0,73 (0,51 - 1) 63,09% (56,42% - 73,02%) 80 0,64 (0,24 - 0,83) 0,65 (0,51 - 1) 0 - - 0,04Teacher exp* 5,57 58,11% 0,59 (0,56 - 0,64) 0,41 (0,36 - 0,44) 0,75 (0,68 - 0,82) 0,64 (0,57 - 0,7) 60,14% (58,26% 62,05%) 80 0,75 (0,68 - 0,82) 0,62 (0,57 - 0,7) 0 - - -0,04Reputation and experienc 5,43 61,95% 0,82 (0,79 - 0,84) 0,18 (0,16 - 0,21) 0,76 (0,7 - 0,83) 0,63 (0,55 - 0,73) 59,64% (57,95% - 61,85%) 80 0,76 (0,7 - 0,83) 0,58 (0,55 - 0,73) 0 - - 0,04Limit teacher hours 2,8 71,80% 0,63 (0,57 - 0,68) 0,37 (0,33 - 0,43) 0,52 (0,21 - 0,73) 0,81 (0,63 - 1) 66,79% (61,36% - 73,02%) 80 0,52 (0,21 - 0,73) 0,76 (0,63 - 1) 0 - - -0,04Cost based* 0,78 63,10% 0,01 (0 - 0,02) 0,99 (0,98 - 1) 0,28 (0,19 - 0,33) 0,99 (0,98 - 1) 72,89% (72,61% - 73,11%) 80 0,28 (0,19 - 0,33) 0,99 (0,98 - 1) 0 - - -0,36

Rule Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Default 0% 6,95 - 0 1 0,26 (0,19 - 0,33) 1 73,11% 80 0,26 (0,19 - 0,33) 1 0 - - 0Default 50% 3,45 61,22% 0,51 (0,45 - 0,57) 0,5 (0,43 - 0,55) 0,69 (0,6 - 0,75) 0,68 (0,61 - 0,75) 62,25% (60,77% - 63,9%) 80 0,69 (0,6 - 0,75) 0,66 (0,61 - 0,75) 0 - - -0,05Default 100% 0,9 51,41% 1 0 1 0,48 (0,41 - 0,55) 50,10% 80 1 0,43 (0,41 - 0,55) 0 - - 0,09Student rep* 3,32 68,81% 0,99 (0,96 - 1) 0,01 (0 - 0,04) 0,63 (0,21 - 0,82) 0,73 (0,5 - 1) 63,3% (57,24% - 73,02%) 80 0,63 (0,21 - 0,82) 0,65 (0,5 - 1) 0 - - 0,04Teacher exp* 2,96 58,58% 0,59 (0,54 - 0,65) 0,41 (0,35 - 0,46) 0,75 (0,65 - 0,8) 0,64 (0,56 - 0,7) 60,26% (58,46% - 61,95%) 80 0,75 (0,65 - 0,8) 0,62 (0,56 - 0,7) 0 - - -0,03Reputation and experienc 2,53 61,98% 0,81 (0,76 - 0,87) 0,19 (0,13 - 0,24) 0,77 (0,65 - 0,85) 0,63 (0,53 - 0,73) 59,43% (57,24% 62,15%) 80 0,77 (0,65 - 0,85) 0,58 (0,53 - 0,73) 0 - - 0,04Limit teacher hours 4,49 72,14% 0,56 (0,52 - 0,61) 0,44 (0,39 - 0,48) 0,52 (0,21 - 0,66) 0,8 (0,67 - 1) 66,64% (63,12% - 73,02%) 80 0,52 (0,21 - 0,66) 0,76 (0,67 - 1) 0 - - -0,04Cost based* 0,92 51,94 1 0 0,99 (0,98 - 1) 0,49 (0,4 - 0,57) 50,35% (50,1% - 51,04%) 80 0,99 (0,98 - 1) 0,44 (0,4 - 0,57) 0 - - 0,08

Equal costsTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

Higher checking costsTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

Higher not_checking_cheater_costTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

Rule Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Default 0% 0,9 - 0 1 0 1 100% 80 0 1 0 - - 0

Default 50% 0,9 90,30% 0,51 (0,48 - 0,54) 0,49 (0,46 - 0,53) 0,5 (0,45 - 0,55) 0,5 (0,45 - 0,57) 99,99% 80 0,5 (0,43 - 0,55) 0,5 (0,45 - 0,57) 0 - - 0,4

Default 100% 0,9 90% 1 0 1 0 99,99% 80 1 0 0 - - 0,9

Student rep* 0,8 99,95% 1 (0,98 - 1) 0,01 (0 - 0,03) 1 (0,97 - 1) 0 (0 - 0,03) 99,99% 80 1 (0,97 - 1) 0 (0 - 0,03) 0 - - 0,99

Teacher exp* 0,9 89,98% 0,9 (0,88 - 0,93) 0,1 (0,07 - 0,12) 0,9 (0,83 - 0,94) 0,1 (0,06 - 0,17) 99,99% 80 0,9 (0,83 - 0,94) 0,1 (0,06 - 0,17) 0 - - 0,8

Reputation and experienc 0,85 94,79% 0,98 (0,96 - 0,99) 0,02 (0,01 - 0,04) 0,98 (0,95 - 1) 0,02 (0 - 0,05) 99,99% 80 0,98 (0,95 - 1) 0,02 (0 - 0,05) 0 - - 0,92

Limit teacher hours 0,87 99,86% 0,34 (0,25 - 0,38) 0,66 (0,62 - 0,75) 0,33 (0,27 - 0,4) 0,67 (0,6 - 0,73) 99,99% 80 0,33 (0,27 - 0,4) 0,67 (0,6 - 0,73) 0 - - 0,33

Cost based* 0,89 92,23% 0,49 (0,44 - 0,57) 0,51 (0,43 - 0,56) 0,49 (0,42 - 0,57) 0,51 (0,43 - 0,58) 99,99% 80 0,49 (0,42 - 0,57) 0,51 (0,43 - 0,58) 0 - - 0,41

Rule Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Default 0% 0,69 - 0 1 0,26 (0,19 - 0,32) 1 73,11% 80 0,26 (0,19 - 0,32) 1 0 - - 0Default 50% 0,51 13,15% 5,1 (4,58 - 6,04) 0,49 (0,4 - 0,54) 1,14 (1,02 - 1,32) 0,98 (0,96 - 1) 1,99% (0,93% - 3,59%) 0 - - 80 1,14 (1,02 - 1,32) 0,98 (0,96 - 1) -0,36Default 100% 0,9 11,16% 10 0 1,13 (1 - 1,26) 0,99 (0,97 - 1) 0,01% 0 - - 80 1,13 (1 - 1,26) 0,99 (0,97 - 1) 0,11Student rep* 0,36 53,71 9,9 (9,58 - 10) 0,01 (0 - 0,04) 1,12 (0,27 - 1,55) 0,96 (0,92 - 1) 31,59% (9,53% - 72,28%) 13 0,31 (0,27 - 0,36) 1 67 1,28 (0,73 - 1,55) 0,95 (0,92 - 1) -0,03Teacher exp* 0,41 24,33%% 2,48 (2,08 - 2,92) 0,75 (0,71 - 0,79) 1,28 (1,03 - 1,63) 0,95 (0,92 - 0,99) 18,39% (10,67% - 29,42%) 0 - - 80 1,28 (1,03 - 1,63) 0,95 (0,92 - 0,99) -0,51Reputation and experienc 0,36 34,53% 6,73 (3,68 - 7,13) 0,33 (0,29 - 0,36) 1,27 (1,02 - 1,62) 0,96 (0,91 - 0,98) 17,16% (9,9% - 27,72%) 0 - - 80 1,27 (1,02 - 1,62) 0,96 (0,91 - 0,98) 0,02Limit teacher hours 0,37 54,82% 9,76 (9,63 - 9,83) 0,02 (0,02 0,04) 1,04 (0,25 - 1,73) 0,96 (0,9 - 1) 35,24% (11,92% - 72,28%) 18 0,3 (0,25 - 0,37) 1 62 1,26 (0,99 - 1,73) 0,95 (0,9 - 0,98) -0,1Cost based* 0,4 21,51% 4,11 (3,38 - 4,88) 0,59 (0,51 - 0,66) 1,24 (1,01 - 1,61) 0,96 (0,93 - 0,99) 10,8% (5,78% - 18,24%) 0 - - 80 1,24 (1,01 - 1,61) 0,96 (0,93 - 0,99) -0,37

Rule Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Default 0% 0,9 - 0 1 0 1 100% 80 0 1 0 - - 0Default 50% 4,94 89,98% 0,49 (0,45 - 0,56) 0,51 (0,44 - 0,55) 0,5 (0,43 - 0,57) 0,5 (0,43 - 0,57) 99,99% 80 0,5 (0,43 - 0,57) 0,5 (0,43 - 0,57) 0 - - 0,4Default 100% 9 90% 1 0 1 0 99,99% 80 1 0 0 - - 0,9Student rep* 7,98 99,95% 1(0,99 - 1) 0 (0 - 0,01) 1 (0,98 - 1) 0 (0 - 0,03) 99,99% 80 1 (0,98 - 1) 0 (0 - 0,03) 0 - - 1Teacher exp* 8,15 89,95% 0,9 (0,86 - 0,92) 0,1 (0,08 - 0,14) 0,89 (0,85 - 0,93) 0,11 (0,07 - 0,15) 99,99% 80 0,89 (0,85 - 0,93) 0,11 (0,07 - 0,15) 0 - - 0,79

Reputation and experienc 8,3 94,95% 0,98 (0,96 - 0,99) 0,02 (0,01 - 0,04) 0,98 (0,95 - 0,99) 0,02 (0,01 - 0,05) 99,99% 80 0,98 (0,95 - 0,99) 0,02 (0,01 - 0,05) 0 - - 0,93

Limit teacher hours 3,26 99,86% 0,34 (0,28 - 0,4) 0,66 (0,6 - 0,73) 0,33 (0,25 - 0,42) 0,67 (0,58 - 0,75) 99,99% 80 0,33 (0,25 - 0,42) 0,67 (0,58 - 0,75) 0 - - 0,33

Cost based* 0,98 91,24% 0,01 (0 - 0,03) 0,99 (0,98 - 1) 0,01 (0 - 0,03) 0,99 (0,98 - 1) 100% 80 0,01 (0 - 0,03) 0,99 (0,98 - 1) 0 - - -0,08

Higher learning_costsTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

Higher caught_cheating_costsTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

Higher checking and learning_costsTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

Rule Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Default 0% 0,69 - 0 1 0,26 (0,21 - 0,32) 1 73,11% 80 0,26 (0,21 - 0,32) 1 0 - - 0Default 50% 4,55 13,09% 5,19 (4,71 - 5,63) 0,48 (0,44 - 0,53) 1,13 (0,99 - 1,37) 0,98 (0,95 - 1) 1,97% (0,73% - 3,05%) 0 - - 80 1,13 (0,99 - 1,37) 0,98 (0,95 - 1) -0,35Default 100% 9 10,98% 10 0 1,11 (1 - 1,26) 0,99 (0,97 - 1) 0,01 % 0 - - 80 1,11 (1 - 1,26) 0,99 (0,97 - 1) 0,11Student rep* 1,59 56,00% 9,93 (9,79 - 10) 0,01 (0 - 0,02) 1,04 (0,24 - 1,7) 0,96 (0,9 - 1) 36,19% (11,92% - 72,28%) 22 0,3 (0,24 - 0,35) 1 58 1,32 (0,98 - 1,7) 0,95 (0,9 - 0,98) -0,13Teacher exp* 2,43 24,10% 2,33 (1,75 - 2,92) 0,77 (0,71 - 0,83) 1,29 (1,06 - 1,6) 0,95 (0,92 - 0,98) 18,32% (11,92% - 26,89%) 0 - - 80 1,29 (1,06 - 1,6) 0,95 (0,92 - 0,98) -0,53Reputation and experienc 2,44 34,83% 6,72 (6,29 - 7,17) 0,33 (0,28 - 0,37) 1,29 (0,94 - 1,52) 0,95 (0,93 - 0,99) 16,64% (7,88% - 28,56%) 0 - - 80 1,29 (0,94 - 1,52) 0,95 (0,93 - 0,99) 0,02Limit teacher hours 1,56 55,30% 9,81 (9,63 - 9,96) 0,02 (0 - 0,04) 1,01 (0,23 - 1,73) 0,96 (0,9 - 1) 36,81% (11,92% - 72,28%) 22 0,32 (0,23 - 0,37) 1 58 1,27 (0,9 - 1,73) 0,95 (0,9 - 0,98) -0,14Cost based* 0,76 73,93% 0,06 (0 - 0,13) 0,99 (0,99 - 1) 0,36 (0,2 - 0,59) 0,99 (0,98 - 1) 71,01% (67,98% - 73,11%) 80 0,36 (0,2 - 0,59) 0,99 (0,98 - 1) 0 - - -0,25

Rule Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Default 0% 9 - 0 1 0 1 100% 80 0 1 0 - - 0Default 50% 4,93 90,07% 0,5 (0,47 - 0,55) 0,5 (0,45 - 0,53) 0,5 (0,42 - 0,57) 0,5 (0,43 - 0,58) 99,99% 80 0,5 (0,42 - 0,57) 0,5 (0,43 - 0,58) 0 - - 0,4Default 100% 0,9 90% 1 0 1 0 99,99% 80 1 0 0 - - 0,9Student rep* 0,84 99,95% 1 (0,99 - 1) 0 (0 - 0,01) 1 (0,97 - 1) 0 (0 - 0,03) 99,99% 80 1 (0,97 - 1) 0 (0 - 0,03) 0 - - 1Teacher exp* 1,67 89,95% 0,9 (0,87 - 0,92) 0,1 (0,08 - 0,13) 0,91 (0,86 - 0,96) 0,09 (0,04 - 0,14) 99,99% 80 0,91 (0,86 - 0,96) 0,09 (0,04 - 0,14) 0 - - 0,8Reputation and experienc 1,06 94,77% 0,98 (0,96 - 0,98) 0,02 (0,02 - 0,04) 0,97 (0,95 - 1) 0,03 (0 - 0,05) 99,99% 80 0,97 (0,95 - 1) 0,03 (0 - 0,05) 0 - - 0,92Limit teacher hours 6,27 99,86% 0,32 (0,27 - 0,39) 0,68 (0,61 - 0,73) 0,33 (0,26 - 0,4) 0,67 (0,6 - 0,74) 99,99% 80 0,33 (0,26 - 0,4) 0,67 (0,6 - 0,74) 0 - - 0,33Cost based* 0,9 90,14% 1 0 1 0 99,99% 80 1 0 0 - - 0,9

Rule Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Default 0% 6,94 - 0 1 0,26 (0,21 - 0,32) 1 73,11% 80 0,26 (0,21 - 0,32) 1 0 - - 0Default 50% 1,11 12,85% 4,9 (4,58 - 5,25) 0,51 (0,48 - 0,54) 1,15 (1 - 1,4) 0,98 (0,95 - 1) 1,97% (0,82% - 4,21%) 0 - - 80 1,15 (1 - 1,14) 0,98 (0,95 - 1) -0,38Default 100% 0,9 11,01% 10 0 1,11 (1 - 1,3) 0,99 (0,97 -- 1) 0,01% 0 - - 80 1,11 (1 - 1,3) 0,99 (0,97 - 1) 0,11Student rep* 2,48 54,95% 9,97 (9,88 - 10) 0 (0 - 0,01) 1,05 (0,28 - 1,67) 0,96 (0,92 - 1) 35,12% (11,49% - 72,28%) 17 0,31 (0,28 - 0,35) 1 63 1,25 (0,77 - 1,67) 0,95 (0,92 - 0,99) -0,08Teacher exp* 2,03 23,90% 2,47 (2,21 - 2,83) 0,75 (0,72 - 0,78) 1,26 (0,96 - 1,7) 0,96 (0,9 - 0,99) 19,2% (8,19% - 31,18%) 0 - - 80 1,26 (0,96 - 1,7) 0,96 (0,9 - 0,96) -0,51

Reputation and experienc 1,49 34,28% 6,9 (6,38 - 7,25) 0,31 (0,28 - 0,36) 1,26 (0,95 - 1,57) 0,96 (0,93 - 0,99) 16,74% (8,51% - 29,42%) 0 - - 80 1,26 (0,95 - 1,57) 0,96 (0,93 - 0,99) 0,03

Limit teacher hours 2,56 54,70% 9,76 (9,42 - 9,92) 0,02 (0,01 - 0,06) 1,03 (0,27 - 1,67) 0,96 (0,91 - 1) 35,54% (12,36% - 72,28%) 19 0,31 (0,27 - 0,38) 1 61 1,26 (1 - 1,67) 0,95 (0,91 - 0,98) -0,12

Cost based* 0,54 18,88% 10 0 1,09 (0,97 - 1,25) 0,99 (0,97 - 1) 1,37% (0,54% - 3,31%) 0 - - 80 1,09 (0,97 - 1,25) 0,99 (0,97 - 1) 0,19

Environment Whole system

Costs Hours accurancy Costs Grade Costs Grade Cheat. prob. Number Costs Grade Number Costs Grade Gain

Software based 1,4 40,83% 5,62 (5,33 - 5,8) 0,3 (0,28 - 0,33) 5,24 (4,7 - 5,51) 0,72 (0,63 - 0,87) 36,65% (23,15% - 52,5%) 3 4,74 (4,7 - 4,78) 0,68 (0,65 - 0,72) 87 5,25 (4,94 - 5,51) 0,72 (0,63 - 0,87) 0Busy teacher 3,93 85,76% 1,62 (1,35 - 1,78) 0,6 (0,55 - 0,66) 1,48 (0,11 - 1,87) 0,74 (0,65 - 1) 85,03% (80,48% - 95,18%) 90 1,48 (0,11 - 1,87) 0,74 (0,65 - 1) 0 - - 0,13Oral exam 6,65 11,14% 10 0 7,2 (7,08 - 7,32) 0,93 (0,89 - 0,97) 4,9%% 0 - - 90 7,2 (7,08 - 7,32) 0,93 (0,89 - 0,97) 0,11

Higher checking and cought_cheating _costsTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

Higher not_checking_cheater and learning costsTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

Higher not_checking_cheater and cought cheating_costsTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

Realistic environemntTeacher Static students Dynamic students

Cheaters Whole set Cheaters Non-cheaters

B Norms compared by parasitic behavior

The following three pages display the amount of parasitic behavior in each ofthe nine scenarios described in section 4.1. The first two columns display theaverage grade of static and dynamic cheating students. By this statistic, wecan observe how well did a particular norm grade static and dynamic cheatingstudents. Some norms did well on static, but bad on dynamic student. Basedon the average grades of these two groups of students I calculated the averagecheating behavior. The cheating behavior was calculated as follows:

NRSC43 ∗ SCG44 + NRDC45 ∗DCG46

NRSC + NRDC

The results of this calculation presents the amount of cheating behavior,which presents the average grade that the cheaters collected in different sce-narios.

43NRSC - Number of static cheating students.44SCG - Average grade of static students.45NRDC - Number of dynamic cheating students.46DCG - Average grade of dynamic cheating students.

45

NORM STATIC DYNAMIC

Default 0% 1 1

Limit teacher hours 0,33 0,76

Cost based* 0,56 0,71

Default 50% 0,5 0,67

Teacher exp* 0,41 0,62

Student rep* 0 0,65

Reputation and experience 0,2 0,58

Default 100% 0 0,44

NORM STATIC DYNAMIC

Default 0% 1 1

Cost based* 0,99 0,99

Limit teacher hours 0,37 0,76

Default 50% 0,5 0,66

Teacher exp* 0,41 0,62

Student rep* 0 0,65

Reputation and experience 0,18 0,58

Default 100% 0 0,44

NORM STATIC DYNAMIC

Default 0% 1 1

Limit teacher hours 0,44 0,76

Default 50% 0,5 0,66

Teacher exp* 0,41 0,62

Student rep* 0,01 0,65

Reputation and experience 0,19 0,58

Cost based* 0 0,44

Default 100% 0 0,43

Equal costs*

CHEATING BEHAVIOR1,00

0,71

0,69

0,65

0,60

0,58

0,54

0,39

Higher checking costs*

CHEATING BEHAVIOR1,00

0,99

0,72

0,64

0,60

0,58

0,54

0,39

Higher not_checking_cheater_cost*

CHEATING BEHAVIOR1,00

0,72

0,64

0,60

0,58

0,54

0,39

0,38

NORM STATIC DYNAMIC

Default 0% 1 1

Limit teacher hours 0,66 0,67

Cost based* 0,51 0,51

Default 50% 0,49 0,5

Teacher exp* 0,1 0,1

Reputation and experience 0,02 0,02

Student rep* 0,01 0

Default 100% 0 0

NORM STATIC DYNAMIC

Default 0% 1 1

Teacher exp* 0,75 -

Limit teacher hours 0,02 1

Cost based* 0,59 -

Student rep* 0,01 1

Default 50% 0,49 -

Reputation and experience 0,33 -

Default 100% 0 -

NORM STATIC DYNAMIC

Default 0% 1 1

Cost based* 0,99 0,99

Limit teacher hours 0,66 0,67

Default 50% 0,51 0,5

Teacher exp* 0,1 0,11

Reputation and experience 0,02 0,02

Default 100% 0 0

Student rep* 0 0

Higher learning_costs*

CHEATING BEHAVIOR1,00

0,67

0,51

0,50

0,10

0,02

0,00

0,00

Higher caught_cheating_costs*

CHEATING BEHAVIOR1,00

0,75

0,65

0,59

0,57

0,49

0,33

0,00

Higher checking and learning_costs*

CHEATING BEHAVIOR1,00

0,99

0,67

0,50

0,11

0,02

0,00

0,00

NORM STATIC DYNAMIC

Default 0% 1 1

Cost based* 0,99 0,99

Teacher exp* 0,77

Limit teacher hours 0,02 1

Student rep* 0,01 1

Default 50% 0,48

Reputation and experience 0,33

Default 100% 0

NORM STATIC DYNAMIC

Default 0% 1 1

Limit teacher hours 0,68 0,67

Default 50% 0,5 0,5

Teacher exp* 0,1 0,09

Reputation and experience 0,02 0,03

Default 100% 0 0

Cost based* 0 0

Student rep* 0 0

NORM STATIC DYNAMIC

Default 0% 1 1

Teacher exp* 0,75 -

Limit teacher hours 0,02 1

Student rep* 0 1

Default 50% 0,51 -

Reputation and experience 0,31 -

Default 100% 0 -

Cost based* 0 -

Higher checking and cought_cheating _costs*

CHEATING BEHAVIOR1,00

0,99

0,77

0,69

0,69

0,48

0,33

0,00

Higher not_checking_cheater and learning costs*

CHEATING BEHAVIOR1,00

0,67

0,50

0,09

0,03

0,00

0,00

0,00

Higher not_checking_cheater and cought cheating_costs*

CHEATING BEHAVIOR1,00

0,75

0,66

0,63

0,51

0,31

0,00

0,00