A Serious Game on Sustainable Development Using Agent Based Modelling - Energy Wars

André Gonçalo Liberato Folgado Under supervision of Prof. Tânia Alexandra dos Santos Costa e Sousa

and Prof. Tanya Vianna de Araújo Instituto Superior Técnico (IST), Lisbon

May, 2014

Abstract

The continuing depletion of natural resources and their increasing exploration costs have become a major focus for society. There is a growing recognition of the need to sustain an ecologically-balanced environment, while at the same time, the need of exploiting natural resources (at affordable prices) to satisfy the ever-increasing demands. Serious Games are a tool that can add entertainment to the increase awareness towards a more sustainable future.

In this work, an energy model was developed to become the serious part of a Serious Game – Energy Wars. The game pretends to simulate an oil scarcity scenario, where energy supply has to be progressively substituted by renewable resource. The objective of this game is to simulate the dispute for the last fossil fuels available on Earth. A platform was built to reproduce the game’s environment. This platform is built under compromises between the reproduction of how real world works and the restrictions imposed by game designers.

The model here presented intends to provide tools to simulate the energy companies’ behaviour. These companies are able to compete for new concessions through auctions, build structures, hire units, and produce energy to supply market’s demand. At each iteration, a company sees all possible investment options and based on its present situation and intentions, can choose according to its goals. A fields’ evaluation framework was developed, computing several economic metrics to support the decision process. This evaluation shows that oil fields have higher return when compared with renewable resources, however, with a higher risk.

Keywords: Serious Games, Oil&Gas, Energy Projects’ Evaluation, Forecasting, Monte Carlo, Risk Management

1 Introduction

This works describes a model that aims to simulate oil/energy companies in a scarcity scenario. However, this work has the objective to provide, along with other models, the background for a game called – Energy Wars (EW). This means that the model has to be contextualized in a gaming reality. This game aims, besides entertainment, to raise the awareness on sustainable development, which defines it as a – Serious Game. Compared with similar products, this game has the following innovative characteristics: (1) it includes an agent-based financial model that captures oil prices fluctuations that are essential to understand this commodity’ behaviour; and (2) also includes a framework to access investment risks on energy’s projects, such as oil exploitation investments. A platform was developed to include all game’s aspects pretended by Biodroid. The platform allows the player to perform actions such as build facilities, hire units that can attack opponents and contract scientists to research, allowing the release of new features. This platform simulates the environment where the energy companies are immersed. Energy related elements were given more detail. An oil upstream industry was simulated since its exploration to the production phase. Were implemented aspects like: (1) licensing; (2) fiscal systems; (3) field evaluation; (4) auctions; and (5) production profile were included. Renewable technologies were evaluated to substitute the natural resources.

The objective of this work is to create a model able to simulate Energy companies´ using agents based modelling (ABM). In the future, this model has to be integrated with a macroeconomic and financial model to serve as a background for a computer game called EW. Being a serious game, this game aims to educate, not just to entertain. Therefore real features had to be included in order to provide some realism to the game. This game creates a scenario where oil is a scarce resource and progressively had to be substituted for renewable resources. Moreover, the model aims to evaluate renewable resources technologies and to propose substitutes for the only natural (finite) resource here considered – Oil. The model’s output pretends to provide decision tools that can be used by an artificial Intelligence (AI) that will be developed by Biodroid. Both the platform and the energy company’s simulation allow to test and advise designers on the game’s balance.

2 Model description

This model is developed to simulate energy companies immersed in a non-real environment. The model can be integrated into the macroeconomic and financial models initially proposed. A balance between games’ aspects, required by Biodroid, and real elements from energy industry had to be made in order to attain the initial objective, which was to use this model (along with the others) as a background for the Energy Wars game.

2.1 Energy Wars

The game pretends to simulate an oil scarcity scenario, where energy supply has to be progressively substituted by renewable resources. World four major companies begin the game in their respective region. Each company can build facilities in different cities, which allows the chief executive officer (CEO) to hire units. The company’s portfolio increases by buying fields and building structures to produce Oil and Electricity. The only source of revenues is selling either forms of energy. When others regions become available, companies are able to expand, build headquarters (HQ) abroad and disputing new fields with host enemies, through auctions. After building a HQ in a new region, the player needs first to build facilities in order to hire their respective units. These characters possess special abilities which allow them to perform specific actions, such as: (1) defend and/or repair to guaranty portfolio security; (2) upgrade structures to increase energy output, and consequently profits; (3) attack enemies; (4) spy to steal technology and corporate data. In Figure 2.1, as it was described before, it is possible to see that there are two different paths that the player can follow when considering his investments – Energy and Non-Energy. The latter captures all sort of options non-energy related. To win the game, the player must reach a victory condition that can be chosen by the player in the beginning of the game (Duarte and Folhadela, 2013): (1) World Tycoon: reaching a net value of a certain amount; (2) World Domination: having more than 50% of the lands; and (3) Oligarch: getting the Oligarch rank.

Figure 2.1 - EW Gameplay

Biodroid provided game’s components to be implemented in the game designed document (GDD) (Duarte and Folhadela, 2013). After careful consideration of the GDD, different clusters were created to group similar components: (1) World; (2) Structures; (3) Companies; (4) Facilities; (5) Units, see Figure 2.2. This was made to facilitate further on the model’s implementation, see section 2.2, where this clusters are used to create classes in the sense of object oriented programing (OOP). The environment can be considered everything that is external to the player’s company, which follows the same interpretation used by agent based modelling (ABM). The environment can have its own identity and/or, for an agent point of view, is any other set of agents (Araújo, 2011). Resuming, is every exterior object on which an agent can apprehend, act or be affected. The environment properties, in the model are both macroeconomics variables, on which companies (i.e. agents) will have to considerate during the decision process and opponents’ data and interactions. In Figure 2.3 a), those properties are classified in four different classes: (1) Economy; (2) Companies; (3) Regions; (4) Cities. In order to organize company’s data, classes were also developed: (1) Fields; (2) Capital; (3) HQ; (4) Decision, see Figure 2.3 b).

Figure 2.2 – EW’s components

a) b)

Figure 2.3 – Model’s properties: a) environment and b) company

2.2 Model Implementation

The software used for the model´s implementation was Matlab®, using an OOP approach. The companies on EW can deliver two types of energy carriers: (1) oil; and (2) renewable electricity. In the model, oil is the only finite natural resource. Its price is given by an agent based pricing model of futures oil contracts, which simulates financial markets behaviour, mainly pretending to capture trader’s speculation (Sousa et al., 2012). There are four different renewable electricity sources: (1) hydro; (2) Solar; (3) Wind; (4) Nuclear. To run this model is necessary to introduce some initial conditions, created to maintain the game’s balance. The space where action unfolds – World – was built to be as flexible as possible, to undertake any resource and rank combination. A tree diagram design was adopted to establish different field’s ranking per resource for each region, seeError! Reference source not found. The steps to configure the world are: (1) define the total number of fields available in the world (t_fields); (2) split t_fields by regions in proportion to their area (Ar), where 𝑟 is defined as the region’s index; (3) for each region, distribute energy resources (D); (4) attribute a rank to each field (R); and (5) percentage of offshore fields per region (O). The control of the fields’ release process is done by four variables: (1) initial period that any field is released (stunlock); (2) mean frequency which a field be released (pull_field); (3) maximum number of fields released at a turn (fields_number); and (4) the overlapping parameter which allows to release higher ranks’ fields, compared with the lower existing ones (overlap). Regarding the field’s resource, it was used a uniformly probability distribution, which means the resource released is equally random. Initially there is defined a period (stunlock), which no field is released. Afterwards, the parameter pull_field, see Equation 2.1, keeps under control the frequency of fields released. Similarly of what was done to characterised offshore fields, a random variable with normal distribution is obtained. If pull_field is signed positive, then the field is released.

𝑝𝑢𝑙𝑙_𝑓𝑖𝑒𝑙𝑑 = {

1, 𝑁(0.2 , 0.5) ≥ 00, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

2.1

Figure 2.4 - World tree diagram

Using, in the Equation 2.1, the values (𝜇 = 0.2 , σ2 = 0.5), the fields are expected to be released every 2/3 turns, as requested by the game designers. Furthermore, if pull_field is positive then fields_number is used to obtain the number of fields to be freed. A random integer between 1 and fields_number is computed, with uniform probability, to decide the amount of fields for companies to purchase at a turn. Finally the overlap parameter depends on the number of lower rank fields available. As less low ranks exist, higher is the probability of higher ranks fields to be released. For every region where companies are already installed, i.e. that have at least one HQ, every investment option is procured within each decision cluster released. The clusters defined were: (1) build HQ; (2) buy lands; (3) build structures; (4) upgrade structures; (5) build facilities; (6) Hire units; (7) Attack; (8) Repair; (9) move units; and (10) Research. The points (2)-(4) are Energy investments, see Figure 2.1. Points (5)-(9) are Non-Energy investments, while both (1) and (10) can be applied for both investment types. This implementation allows to know the exact amount of states to which the company can evolve at every turn proved to be advantageous to the model. To know the complete list of actions at every turn, made it possible to be a model that can be played.

It was used deliberative agents to apply ABM. This agents use a Belief-Desire-Intention (BDI) architecture (Bratman, 1987). The main components for the BDI agent’s architectures are: (1) beliefs, (2) desires, (3) intentions and (4) plans, see Figure 2.5. BDI theory is based on the philosophy of practical reasoning (Bratman, 1987). It offers flexibility in planning by reasoning over different goals.

Figure 2.5 - BDI agent's architecture (Bratman, 1987)

Figure 2.6 a) describes a game’s, turn from a player point of view. A turn begins with the player perceiving the changes in the environment, updating his data from all classes, Figure 2.3 b). Afterwards, the player evaluates its current status within his ambience and execute every actions for him available. To organize the company’s agenda, due to the actions’ time lag, the investments undertook were classified into three different categories: (1) Planned; (2) Pending; and (3) Executed. If a concession is released in the present turn, company will assess its net present value (NPV) estimation. Moreover if at least one more opponent had intended to purchase the same field, an auction will take place, see section 3.4. Considering all investments made its time for the company to update its status. All expenditures are deducted in company’s current capital. Both Oil and Electricity produced are sold at the actual respective price.

Figure 2.6 represents a diagram of the main script source code. It is possible to observe that it has a structure of a BDI agent. At first, the model initialize by reading the initial conditions. Afterwards builds the game components: (1) Configures the world, (2) creates companies and (3) constructs the environment. Thereafter, at each turn, is randomly assigned players’ playing order. Companies start their course of actions by perceiving the changes in the environment, seeError! Reference source not found., and update company’s beliefs, which are the new available fields and both oil and electricity prices. Afterwards it procure all investment options at that turn. In the model companies invest on every possible action. Subsequently, these actions are scheduled in the Planned matrix’s status. Afterwards, all actions assigned in the Execute matrix are executed at the present turn. Each action undergoes the following procedure, when being executed: (1) identify the action cluster that it belongs; (2) find an available unit to perform the action; (3) randomly attributes a unit and execute the action; (4) updates company properties, discounts the actions costs and subtracts the used unit; and (5) if it is the case, releases a decision cluster or a feature. The last operations to be done in the companies’ cycle are: (1) to receive do the payments; (2) update the reserves values and (3) update the aggregate production. Thereafter, if more than one company decides to purchase the same field, an auction will occur, see section 3.4. The last operation is to verify if any victory condition was achieved. If any company achieves such condition the game ends and that company is declared the winner. If it does not happen, the game continues until the maximum number of turns is reached.

a) b)

Figure 2.6 – Model’s algorithm diagram

3 Decision Process

The energy related aspects were the most developed throughout the model. The methodology used for projects’ evaluation and risk analysis is similar to those used in the real world, especially the ones concerning the natural resource field’s evaluation. In the model, the only source of revenues comes from both oil and electricity sold into the market. Therefore the only way to increase the revenues is to purchase more fields and upgrade structures. Biodroid wanted that the fields’ acquisition were made through auctions, see section 3.4, in order to increase competition between companies. Consequently, companies had to be able to access what was the field’s economic interest.

An evaluation model was developed to enable companies to estimate the concession’s economic potential, as in Pergler and Rasmussen (2014). The objective was that companies could compute a bidding value for the available fields. Furthermore, this model allows companies to forecast several scenarios (n_sim) using Monte Carlo (MC) on both energy carriers’ prices and interest rates, see section 3.1. With this information, is developed a discount cash flow model to achieve a concession’s net present value (NPV). Moreover, applying a normal distribution fitting on the n_sim NPV obtained, are able to make an investment field analysis. When a field becomes available to be acquired, a company can decide if proceeds for the field’s evaluation or not. A field can only be bought if it has been evaluated. Companies know beforehand: (1) the number of opponents within the same region; (2) current energy’s prices; and (3) fields’ properties.

3.1 Energy Prices and Interest Rates Forecast To forecast either energy prices’ or interest rates it was implemented two models: (1) a geometric Brownian motion (GBM), see Equation 3.1, and a mean reverting model MRM, Equation 3.2. Brent prices were chosen because they represent two thirds of the oil commercialized in the market (ICE Crude & Refined Oil Products, 2014). The GBM was used to simulate the oil prices, while the MRM was used on the residential and industrial average end-user (after tax) prices modelled by Brito (2013) and the real interest rates. However, there is not any empirical evidence, of a model proved to be better than other to forecast either commodity prices or real interest rates (Meade, 2010).

dX𝑡 = 𝜇(𝑡)𝑋𝑡𝑑𝑡 + 𝐷(𝑡, 𝑋𝑡)𝑉(𝑡)𝑑𝑊𝑡 3.1

dX𝑡 = 𝑆(𝑡)[𝐿(𝑡) − 𝑋𝑡]𝑑𝑡 + 𝑉(𝑡)𝑑𝑊𝑡 3.2

The MC approach has the benefits that the accuracy of the density forecasted is far more informative than the accuracy of a forecasted point or a prediction interval (Meade, 2010). Each scenario is a set of both price and interest rate’s simulations. A simulation is a result of the random walks set from the last known value, where the number of random walks performed is defined as n_sim. The number of values that are pretended to be forecasted, from the last data known (i.e. the current month) is defined as nv_forcast. Therefore, each random walk will evolve from the current month to nv_forcast months ahead. In the case of the oil fields, nv_forcast is the amount of turns that a field is expected to last, defined as 𝑀. The parameter nv_forcast was calculated by dividing the field’s reserves by the average production available (𝑞𝑗̅̅ ̅), i.e. production level II. The renewable resources’ are infinite, therefore to evaluate a concession should be discounting a perpetuity cash flow (Brealey and Myers, 2012). However to simplify the implementation, i.e. to not have different discount formulas, a sufficient large nv_forcast was selected for the effect on the NPV be less than 1%, see section Error! Reference source not found.. Therefore, when used to discount oil fields nv_forcast is equal to 𝑀, while on renewable fields, the value is fixed to 100.

Both models need data to be calibrated. As stated above each turn represents a month. One of the inputs is the Initial Date, which represents the first, i.e. old, data retrieve to from the database to calibrate the models. This parameter can be of great significance because if a most recent Initial Date is chosen, it can suppress the influence of long age data that was affected by special events, such as wars, e.g. World War I (1914-1918), Six-day War (1967) and recently the Iraq’s conflict (2003). Moreover, oil embargos can also cause considerable supply shocks or even disruptions, such as the Suez Crisis (1956) and UN Iraq embargo (1990) (Yergin, 2011). Consequently, these incidents have great impact on energy prices resulting on significant effects on countries’ macroeconomics (Barsky and Kilian, 2004). For every evaluation, each model does a thousand simulations (𝑛_𝑠𝑖𝑚 = 1000), to perceive the scenarios’ statistical properties stabilization. The inputs necessary to run the models are in Table 3.1.

Table 3.1 – Inputs for the forecast models

n_sim nv_forcast Model Initial Date Historical Data

OIL 1000 𝑀 GBM Jan -1991 (BP - Statistical review of world energy,

2012)

ELECTRICITY 1000 100 MRM Jan - 1991 (Brito and Sousa, 2013)

INTEREST RATES

1000 𝑀 MRM Jan - 1991 (Interest Rates and Yields - Money

Market - Monthly, 2014)

Figure 3.1 illustrates a Brent oil prices forecasting using a MC simulation. As mentioned above the benefits of the MC approach is the information provided by the forecasted density. In Figure 3.1, random walks start from Feb-2013 (inclusive). It is possible to perceive a clear trend for the oil price to increase. Moreover, is observed a higher density between the 100 $ and 200 $ interval. Therefore is possible to deduce that crude oil Brent prices should remain high, above 100 $, with a clear tendency to increase (Annual Energy Outlook, 2014). The GBM model experience some overshooting prices’ random walks, which could be explained by data calibration relative to the Iraq War (2003) and the start of last economic crises (2008).

Figure 3.1 – MC simulation of Brent crude oil prices

Error! Reference source not found. illustrates North America residential and industrial average end-user (after tax) prices. It is clear a decreasing trend of the electricity prices in the following two decades, which is in line with the results of Brito (2013). Is observed a forecasted density between the 9 US₵ and 12 US₵ interval. This result is in consonance with the last Annual Energy Report (Annual Energy Outlook, 2014) from the Energy Information Agency (EIA). The EIA report forecast that residential prices stabilize around the 9 US₵ and the industrial price will secure around 12 US₵. The average prices, including residential, commercial industrial and transportation, will stabilize around 11 US₵. Due to the MRM mean reverting characteristic, the electricity prices random walks, see Error! Reference source not found. are much better behaved when compared with the oil prices, see Figure 3.1.

It can be observed in Figure 3.2 b) an annual average of the simulated real interest rates. It is perceived the mean reverting property of the MRM model. The interest rate tend to oscillate around 2%, which is in consonance with the last report from the International Monetary Fund (World Economic Outlook: Recovery Strengthens, Remains Uneven, 2014).

a) b) Figure 3.2 – MC MRM forecast of a) annual average of the real interest rates simulation average and b) North America

end-user electricity prices

3.2 Field’s Economics

Four key economic metrics are used to rank projects and to decide whether or not they should be accepted for inclusion in the capital budget: (1) discounted payback (DP), (2) NPV, (3) internal rate of return (IRR), (4) profitability index (PI) (Brealey and Myers, 2012). To compute these parameters was necessary to develop a discounted cash flow model to calculate economic metrics to support the decision making, see Table 3.2. Moreover, elements from the fiscal system type adopted – concession (Tordo, 2007) – were also include:(1) royalties (𝑟𝑜𝑦𝑦); and (2) corporate taxes (𝑡𝑎𝑥𝑦). The forecasted prices and interest rates were computed in a

monthly period, the same as a game’s turn. Consequently, a year average had to be applied to both in order to fit the data in the discount cash flow process. The year averages of price and discount rate correspond to 𝑝𝑦

and 𝑟𝑦, respectively. The capital expenditure (𝑐𝑎𝑝𝑒𝑥) is obtain multiplying 𝑞𝑗̅̅ ̅ by the specific capital costs for each

resource 𝑗, within region 𝑟 ( 𝑟𝑐𝑐𝑗,𝑟).

Table 3.2 – Discount cash flow model

Financial variables Year 0 Yr 𝒚 Units Equations/References

Oil production 𝒒𝒚 Mbbl 𝒒𝒋

Oil price 𝒑𝒚 $/bbl 3.1

SALES REVENUE 𝒓𝒆𝒗𝒚 MM$ 𝒒𝒚 × 𝒑𝒚𝒊

Transport Costs

𝒕𝒄𝒚 MM$ 𝟎

OPEX Production Costs 𝒑𝒄𝒚 MM$ (Brito and Folgado, 2012)

Royalty 𝒓𝒐𝒚𝒚 % 7%*

Depreciation 𝒅𝒆𝒑𝒚 % 𝟓%*

OPERATIONAL EXPENDITURE 𝒐𝒑𝒆𝒙𝒚 MM$ (𝒓𝒆𝒗𝒕)𝒓𝒐𝒚𝒚𝒓 − 𝒕𝒄𝒚 − 𝒑𝒄𝒚

TAX Corporate Tax 𝒕𝒂𝒙𝒚 % 𝟑𝟓%*

OPERATING PROFIT BEFORE TAX 𝒑𝒓𝒐𝒇𝒊𝒕_𝒃𝒕𝒚 MM$ 𝒓𝒆𝒗𝒚 − 𝒐𝒑𝒆𝒙𝒚

CAPEX Capital Expenditure 𝒄𝒂𝒑𝒆𝒙 MM$ 𝒓𝒄𝒄𝒋,𝒓 × 𝒒�̅� UNDISCOUNTED NET CASH FLOW 𝒄𝒇𝒚 MM$ (𝒑𝒓𝒐𝒇𝒊𝒕_𝒃𝒕𝒚)(𝟏 − 𝒕𝒂𝒙𝒚

𝒓)

Discount rate 𝒓𝒚 % 3.2

Cash Flow Present Value MM$

Cumulative Cash Flow MM$

NPV 𝐍𝐏𝐕𝒑

𝒊

MM$ − 𝒄𝒂𝒑𝒆𝒙 + ∑ 𝒄𝒇𝒚 (𝟏 + 𝒓𝒚𝒊 )

𝒚⁄

𝒀

𝒚=𝟎

IRR 𝑰𝑹𝑹𝒑

𝒊

% ∑ 𝐜𝐟𝒚 (𝟏 + 𝑰𝑹𝑹𝒑𝒊 )

𝒚⁄

𝒏

𝒕=𝟎

= 𝟎

DP 𝑫𝑷𝒑𝒊 Years (Brealey and Myers, 2012)

PI 𝐏𝐈𝒑𝒊 % 𝐍𝐏𝐕𝒑

𝒊 𝒄𝒂𝒑𝒆𝒙⁄

Legend: Costs; Decision Variables * World Average (Tordo, 2007)

The discount cash flow model is applied for the n_sim scenarios obtained from the MC method. Thereafter, is computed n_sim values for each economic metrics, resulting in vectors with size equal to n_sim.

3.3 Risk Management

A normal distribution fitting is applied on the NPV vector. This process was only applied for the NPV, although it can be performed to any of the economic metrics. Due the high number of simulated scenarios (𝑛_𝑠𝑖𝑚 = 1000) is possible to extract significant statistical properties from normal distribution, see Figure 3.3.

Figure 3.3 - NPV normal distribution fitting

A long right tail indicate a considerable amount of outliers. The reason is the influence of the oil price simulations. Due to the oil historical prices, affected for instance by Iraq War (2003) and the financial crises in 2008, simulated random paths tend easily to increase drastically, see Figure 3.1. Four different scenarios are evaluated in the normal fitting: (1) NPV’s mean value; (2) NPV’s confidence level at 95%; (3) zero profit probability; and (4) Base case with 100$ a barrel. Figure 3.3 shows the results of the concession’s evaluation model applied on a bronze

oil field (6 Gb) situated in North America ( 𝑟𝑐𝑐𝑗,𝑟 = 8.445 [$ 𝑏𝑏𝑙⁄ ]). It was used for oil fields, a constant level II

production, 𝑝𝑗 = 3 𝑀𝑏𝑏𝑙/𝑚𝑜𝑛𝑡ℎ through all projects’ life cycle. By analysing the figure is possible to perceive that this is an investment with a low risk of being unprofitable. It has 2% probability of having negative NPV. Also, at a confidence level at 95% has PI equal to 3.44 (244 %) and mean IRR of 40.66 %. This values are unrealistic (significantly higher than found in literature) because they use the model’s calibration for the desired game’s balance, which is this works propose. Table 3.3 resumes the concessions evaluation results. It is presented project’s economic metrics mean values for an oil field and an electricity project. Comparing both Bronze fields, oil and solar, it is clearly that the oil investment is much more profitable than the solar one. However, oil NPV

mean standard deviation σNPV𝑝𝑖 is relatively higher, with the same order of magnitude that the mean expected

NPV μNPV𝑝𝑖 . While in the solar case, the investment is less profitable, although the risk is considerably inferior.

Table 3.3 – Project’s economic metrics mean values results

FIELD 𝛍𝐍𝐏𝐕𝒑

𝒊

$MM

𝛔𝐍𝐏𝐕𝒑𝒊

$2MM

𝐏𝐈𝒑𝒊

%

𝑰𝑹𝑹𝒑𝒊

%

𝑫𝑷𝒑𝒊

YEARS LCOE

EU_O_B_3 882 228 201 147 244 40.66 3.25 8.88 $/bbl

EU_S_B_1 78.191 5.073 11 19.02 16.51 0.19 $/kWh

3.4 Auction The selected block’s license, necessary for companies to explore and exploit natural resources within the host countries, were the auctions. Before any proposal, first companies should analyse two key elements: (1) the fiscal, system suggested by the host governments; and (2) geologic properties of the block, such as porosity and permeability (Gomes and Alves, 2011). The latter is outside of this work’s scope, where is assumed that are known the initial reserves of the license area. The fiscal system defined for all fields were the concessions, as discussed above. The type of auction selected was a derivation of the first sealed price (Tordo et al., 2010). At a turn if more than one company desire to acquire the same field, an action occurs. As mentioned, companies order is randomly established in the beginning of each turn. The first in line gets to hold a token that allows him to bid twice in the same turn. This twist gives to the token holder the options to deviate the field from an opponent if the highest bid is not his own. Due to the risks and uncertainties, companies bid based in a worst-case scenario. Therefore, the NPV at a 95% confidence is the value used. After estimate the fields’ profitability, a random value is computed between 0 and 1 with a distribution positive skewed, i.e. higher probability of getting a value close to 0. This implementation is consistent with Afualo (1997), which affirms that: “the bid underestimates value, since the bidder is bidding for some profit”. The random distribution used was the exponential, with mean parameter 𝑚𝑢 equal to 0.1 (𝑚𝑢 = 0.1), see Equation 3.3, to expect bidding prices

around 1% of NPV𝑝𝑖 (5%).The software’s random algorithm provides 𝑥 at each call.

bid𝑝

𝑖,1 =1

𝑚𝑢𝑒

−𝑥𝑚𝑢 × NPV𝑝

𝑖 (5%) 3.3

The company that hold the token , offers 1% more, than the highest bid if decides to acquire the field, is defined

as bid𝑝𝑖,2. The decision to buy is related with the current capital. If the highest bid is inferior than25% of company’s

current capital, the field is purchased.

4 Conclusion and Future Work

The main objective of the model was to provide decision tools to use in the decision process being implemented posteriorly. The decisions that were made throughout the conception of the model took into account the trade-off between the flexibility needed by the game designers and the increasing complexity of the model. To aim for the game’s scenario, where oil was a scarce resource that progressively is being substituted by renewable resources, the oil upstream industry was implemented to capture all oil process since its exploration to market. To calibrate the model with costs and production values that are similar to reality, a database was developed together with Mário Brito. Although the main objective for the model is to support the Energy Wars development it can be calibrated to be used for more serious studies. Using the model implementation for the world configuration, it is possible to replicate the finite amount available of natural resources. Regarding the renewables resources, they could be calibrated for the maximum power capacity. Ranks could be a fuzzy classifications for different irradiation levels (solar) or average wind velocities (wind). Throughout the model implementation, it was concluded t_fields can have huge impact. If it is small (e.g. 100), companies rapidly feel

the necessity to explore new regions and the interaction between players happens much earlier in the game. While in the opposite case, companies might not leave the region where they have started. In the current condition, the model is driven by the new fields that are being released. After an initial period, where companies do everything that is possible to do, they achieve a state where no more HQ and facilities can be built and the limit of units have been reached. This happens because only energy related actions were fully implemented. Regarding to the forecasting exercise of energy prices and interest rates the conclusions obtained are the following. Oil Brent price is observed to have a higher forecasted density between the 100 $ and 200 $ interval. Therefore, it is possible to deduce that crude oil Brent prices should remain high, above 100 $, with a clear tendency to increase. While for the residential and industrial average end-user electricity price, is observed that the forecasted density stabilizes between the 9 US₵ and 12 US₵ interval. Furthermore, the GBM model experiences some overshooting prices’ random walks, which could be explained by the data calibration relative to the Iraq’s War (2003) and the start of last economic crises (2008). It is observed the mean reverting property of the real interest rate that tend to oscillate around 2%. It can be concluded in the results of the risk analysis for both fields, oil and solar, that an investment on an oil field can be much more profitable comparing with a renewable one. However, implies a considerable higher risk, which can be explained by the oil prices’ volatility.

The main focus of an extension to this work should be the development of an independent AI. In this thesis, is proposed a partial observed Markov decision process (POMDP) to support companies in the decision process. Game theory aspects could be implemented both in auction and on the quantity of product supplied to the market using a Cournot’s Nash equilibrium under Oligopoly market. For this, it would be necessary to provide the ability for companies to store non sold product. An important ABM characteristic that was not addressed in the model is the possibility of bankruptcy, where an agent/company is withdrawn from the market and replaced by other. Moreover, could also be implemented the possibility of a company to contract loans to support any investment.

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