human learning in dynamic environments. coty gonzalez 2008 megr... · /ddml bdu/ddmlab social and...
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Human Learning in Dynamic Human Learning in Dynamic Environments
Cleotilde (Coty) GonzalezDynamic Decision Making Laboratory
d /DDML bwww.cmu.edu/DDMLabSocial and Decision Sciences Department
Carnegie Mellon University
Research supported by the National Science Foundation :
Human and Social Dynamics: Decision, Risk, and Uncertainty
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Dynamic Environments• Combat missions, Production scheduling, Fire fighting,
Emergency dispatch, Air-traffic control• Complex
o Number of components: alternatives, events, courses of action, outcomes
o Uncertainty: All possible states of the world and outcomes are unavailable, incomplete, and difficult to imagineg
o Constraints: limited time, knowledge, resources, human capacity
• Dynamic Complexity• Dynamic Complexityo Arises from the interactions of components over timeo Environment is autonomous. All is change at many g y
different time scaleso Learning from our actions: feedback delays
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Dynamic Decision Making: A Closed-Loop view
Hypothesize illnesses and Symptoms
delay
run tests
delay delay
Test resultsHealth
External event
resultsHealth
DiagnosisTreatment
delay delay
DiagnosisTreatment
delay
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Learning in dynamic systems is hard
• People remain suboptimal in these systems even with repeated trials, unlimited time and performance incentives (Sterman,1994; Diehl & Sterman 1995)Sterman, 1995).
• We have difficulty processing feedback. F db k d l bl f l Feedback delay is a problem for learning (Brehmer, 1992; Sterman, 1989).
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But… how do we learn in dynamic environments?environments?• Decision Makers recognize typical situations and typical
D i i k th i t k l d responses. Decision makers use their past knowledge and adapt their strategies “on the fly”.
Chess studies Expertise: Chase & Simon 1973o Chess studies, Expertise: Chase & Simon, 1973
o Adaptive Decision Making: Payne, Bettman, & Johnson, 1993
o Decision making under uncertainty: “Case-Based Decision o Decision making under uncertainty Case Based Decision Theory” , Gilboa and Schmeidler, 1995
o Theory of automaticity: Logan, 1988
o “Recognition-Primed Decision Making” (RPDM): Intuition, Mental simulations, Klein et al., 1993; Klein, 1998
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Pattern recognition is easier if you have iexperience
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Instance Based Learning Theory (Gonzalez, Lerch, & Lebiere, 2003)
• RECOGNITION OF FAMILIAR PATTERNSo Determining the similarity between a situation and past
experience o Identifying ‘typical’ situations and responsesy g yp p
• ACQUIRING CAUSE-EFFECT KNOWLEDGEQo Accumulation of instances with practice in a task o Improvement of decision making by bootstrapping on previous
k l d knowledge
Implemented in ACT-R (Anderson and Lebiere, 1988)
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IBLT: WHAT do we learn?
Situation Decision OutcomeSituation-Decision
Cycle
Action-Outcome
CycleCycle Cycle
FutureDecisions
S ODS D O
S D O
Blending of past
OutcomesSimilarity
S D OS D O Time
Outcomes
F db kEnvironment
Feedback
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IBLT: HOW do we learn?
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ACT-R(A d & L bi 1998)(Anderson & Lebiere, 1998)
h l l f
Declarative Memory Procedural Memory
The 2x2 levels of ACT-R
Chunks: declarative facts
Productions: If (cond) Then (action)
Symbolic
facts (cond) Then (action)
A ti ti f h k
S bS b li
Activation of chunks (likelihood of
retrieval)
Conflict Resolution (likelihood of use)
SubSymbolic
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IBLT models compare to human decision making:
• In dynamic resource allocation tasks (Gonzalez et
making:
al., 2003)
• In supply chain management control (Martin, Gonzalez & Lebiere 2004)Gonzalez & Lebiere, 2004)
• In repeated choice tasks (Lebiere, Gonzalez & Martin, 2007)2007)
• But there is long way to go to demonstrate: generalizability and utility of IBLTg y y
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Decision Making Games (DMGames) used for experimentationfor experimentation
• DMGames embody the essential characteristics of • DMGames embody the essential characteristics of real-world decision environments
o Interactiveo Interactive
o Repeated and interrelated decisions
E t l t d t i t tio External events and team interactions
• Help compress time and space – speed up learning
• Help manipulate experience - learn from simulated cases and on-demand repeated practice
k d d l d h • No risk to individuals and they are FUN.
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DMGames used in behavioral research in the DDMlab
Military Command and Control
Real-time resource allocation
Military Command and Control
Real-time resource allocationReal time resource allocationReal time resource allocation
Medical Medical
Supply-Chain
ed caDiagnosis
Supply-Chain
ed caDiagnosis
Chain Management Fire
Fighting
Chain Management Fire
Fighting
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MEDIC: Learning tools that represent the dynamics of medical diagnosis (Gonzalez & Vrbin, 2007)y f g ( , )
• Concepts adapted from Kleinmuntz (1985):
Task complexity (numerous diseases and symptoms)o Task complexity (numerous diseases and symptoms)o Disease base rateso Time pressureo Test diagnosticityo Treatment effectivenesso Treatment risko Treatment risk
• Additions:
o Feedback delays (e.g. receiving test results)
• With the potential for:
o Dynamic diagnostic cues
o Dynamic symptoms
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MEDIC demo
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Factors that influence Learning in dynamic systemsy
• Time constraints (Gonzalez, 2004)
• Workload (Gonzalez, 2005)
• The similarity and diversity of experiences (Gonzalez and y y pQuesada, 2004; Gonzalez and Madhavan, in preparation)
• Our inherent cognitive abilities (Gonzalez, Thomas and Vanyukov 2004)Vanyukov, 2004)
• The type of feedback (Gonzalez, 2005)
• Our difficulty in understanding simple stock and flow Our difficulty in understanding simple stock and flow structures (Cronin and Gonzalez, 2005; Cronin, Gonzalez and Sterman, 2006; Gonzalez, Sterman and Cronin, in preparation)
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Experiment 1: probabilities
• MEDIC incorporated:
o Symptoms-disease associations from 0.1 to 0.9
o Delay in test resultsy
o Time pressure due to patient’s declining health in real-time
o Deterministic treatment needed to be provided
• N=12, students, paid flat rateN , students, pa d flat rate
• Each student resolved 56 cases
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Results
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Treatment
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Results- test diagnosticity
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Disease base rates
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Diagnosticity per disease
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Experiment 1: Conclusions
• Students did learn – not perfectly• Showed knowledge of probabilities, tested for the
more diagnostic cues, and diagnosed very closely to the real state of the diseases.f .
• What is the role of feedback and how would that interact with the symptom-probability matrix?
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Experiment 2: Probabilities and f db kfeedback
• MEDIC:• Symptomology table: Probability or Certainty• either detailed feedback or no feedback
• Participants were assigned to one of four conditions:o probabilities, full feedback (P1) -26o certainty full feedback (P2)-30o certainty, full feedback (P2) 30o certainty, no feedback (P3)-25o probabilities, no feedback (P4)- 29
• N= 110 Participants were paid a flat dollar amount
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P b biliProbability
C t i t
Disease 1 Disease 2 Disease 3 Disease 4 0.25 0.25 0.25 0.25 Base Rates
Certainty
0.0 0.0 0.0 0.0 Symptom 11.0 0.0 0.0 0.0 Symptom 2 1.0 1.0 0.0 0.0 Symptom 3 0.0 0.0 1.0 0.0 Symptom 4
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Test diagnosticity - probability condition
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Test diagnosticity – Certainty condition
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Diagnosticity per disease
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Experiment 2: Conclusions
• Full feedback was helpful in the probabilistic i t d did t k diff i th environment and did not make a difference in the
certain environment
• We now know that: with repeated trials, students p ,learn in probabilistic environments with time constraints and feedback delays
• Feedback helps in probabilistic environmentsFeedback helps in probabilistic environments• Probabilistic environments are not the main reason
for poor learning in dynamic tasks
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Basic Building Blocks of Dynamic Decision Making TasksMaking Tasks
• Stocks (accumulations)
• Flows that increase (Inflow) or decrease (Outflow) the stock
• Feedback Delays & multiple relationships
• Environmental or external effects
• Multiple decisions about flows
These problems of dynamic control over time are important to human life: keeping a healthy weight, bank p p g y gaccounts, company inventory, stress levels, climate change etc.
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Humans suffer of poor understanding of accumulation: Stock-Flow failureaccumulation: Stock Flow failure
Cronin, Gonzalez & Sterman, 2008 ; Cronin & Gonzalez, 2007; Cronin, Gonzalez and Sterman, 2006; Sweeney & Sterman, 2000 St 2002 2000; Sterman, 2002;
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Weight as balance between consumed and expended energyexpended energy
1. When eaten most?
2. When exercised most?
3. When weight highest?
4. When weight lowest?4 g
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Blood glucose level as balance between glucagon and insulin productionglucagon and insulin production
1. When most glucagon?g g
2. When most insulin?
3. When glucose level 3. When glucose level
highest?
4. When glucose level 4. When glucose level
lowest?
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Why? (Cronin & Gonzalez, 2007; Cronin, Gonzalez & Sterman, 2008)
• Not an artifact of the graph
t rman, )
• Not due to the form of graphical presentation
• Not due to motivation• Not due to motivation
• Not due to familiarity with the context
• Stock Flow failure is one important reason for • Stock-Flow failure is one important reason for learning problems in dynamic systems
U f h i ti th t i t iti l li • Use of heuristics that are intuitively appealing but erroneous
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Future work
• Further investigate the correlation heuristic and the Stock Flow failureand the Stock-Flow failure
• Use DMGames of Dynamic Stocks and Flows to d t d th i d l i bl understand the reasoning and learning problems
in dynamic tasks
• Further develop the Instance-Based learning theory to other dynamic problems, like the St k FlStock-Flow
• Further investigate ways to identify and overcome the problems in learning in dynamic systems
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DDMLab – February,