Public Goods Games

Many interactive environments can be represented as games, but they are so large and complex that individual players are in the dark about others' actions and the payoff structure. This paper analyzes learning behavior in such 'black box' environments, where players' only source of information is their own history of actions taken and payoffs received. Specifically we study voluntary contributions games. We identify two robust features of the players' learning dynamics: search volatility and trend-following. These features are clearly present when players have no information about the game; but also when players have full information. Convergence to Nash equilibrium occurs at about the same rate in both situations.

Many interactive environments can be represented as games, but they are so large and complex that individual players are in the dark about others' actions and the payoff structure. This paper analyzes learning behavior in such 'black box' environments, where players' only source of information is their own history of actions taken and payoffs received. Specifically we study voluntary contributions games. We identify two robust features of the players' learning dynamics: search volatility and trend-following. These features are clearly present when players have no information about the game; but also when players have full information. Convergence to Nash equilibrium occurs at about the same rate in both situations.

This paper examines cooperation and punishment in a public goods game in Istanbul. Unlike prior within-subject designs, we use a between-subject design with separate no-punishment and punishment conditions. This approach reveals that punishment significantly increases contributions, demonstrating the detrimental effect of having prior experience without sanctions. We highlight two critical factors-heterogeneous initial contributions across groups and how subjects update their contributions based on prior contributions and received punishment. An agent-based model verifies that the interaction between these two factors leads to a strong persistence of contributions over time. Analysis of related data from comparable cities shows similar patterns, suggesting our findings likely generalize if using a between-subject design. We conclude that overlooking within-group heterogeneity biases cross-society comparisons and subsequent policy implications.

The evolution and maintenance of cooperation in human and animal societies challenges various disciplines ranging from evolutionary biology, to anthropology, social sciences and economics. In social interactions, cooperators increase the welfare of the group at some cost to themselves whereas defectors attempt to free-ride and neither provide benefits nor incur costs. The problem of cooperation becomes even more pronounced when increasing the number of interacting individuals. Punishment and voluntary participation have been identified as possible factors to support cooperation and prevent cheating. Typically, punishment behavior is unable to gain a foothold in a population, while volunteering alone can efficiently prevent deadlocks in states of mutual defection but is unable to stabilize cooperation. The combined effects of the two mechanisms have surprisingly different consequences in finite and infinite populations. Here we provide a detailed comparison of the two scenarios and demonstrate that driven by the inherent stochasticity of finite populations, the possibility to abstain from social interactions plays a pivotal role, which paves the way for the establishment of cooperation and punishment.

In human societies, cooperative behaviour in joint enterprises is often enforced through institutions that impose sanctions on defectors. Many experiments on so-called public goods games have shown that in the absence of such institutions, individuals are willing to punish defectors, even at a cost to themselves. Theoretical models confirm that social norms prescribing the punishment of uncooperative behaviour are stable: once established, they prevent dissident minorities from spreading. But how can such costly punishing behaviour gain a foothold in the population? A surprisingly simple model shows that if individuals have the option to stand aside and abstain from the joint endeavour, this paves the way for the emergence and establishment of cooperative behaviour based on the punishment of defectors. Paradoxically, the freedom to withdraw from the common enterprise leads to enforcement of social norms. Joint enterprises which are compulsory rather than voluntary are less likely to lead to cooperation.

Achieving a common goal requires the cooperation of at least some individuals. c This can be modeled by an N-person snowdrift game, a variant of Public Goods Games. c Social networks influence the emerging behavioral dynamics. c Homogeneous networks help the coordination towards stable levels of cooperation. c Heterogeneous networks create a new scenario with multiple behavioral equilibria.

I would like to thank my supervisor, Dr Flávio Pinheiro, for his optimism, for the opportunity created and for providing me guidance and feedback throughout this project. Without his support I would not have been able to complete this research. I would also like to thank my co-supervisor Dr Francisco Santos for his insightful comments on this research. The papers I had the chance to read from them were inspiring and decisive in my decision to undertake this research. I would also like to thank Margarida for her constant encouragement, interest and help in the subjects I studied during the master's curricular component leading to this thesis. Last but not least I want to thank my family and friends for their incentive and for putting up with the time I invested in this master program depriving me of social gatherings.

Achieving a common goal requires the cooperation of at least some individuals. c This can be modeled by an N-person snowdrift game, a variant of Public Goods Games. c Social networks influence the emerging behavioral dynamics. c Homogeneous networks help the coordination towards stable levels of cooperation. c Heterogeneous networks create a new scenario with multiple behavioral equilibria.

It has been pointed that public goods games lack experimental research in topics such as the study of the interaction of groups versus the interaction of individuals and effects of social identity and decision framing. Furthermore, the number of computational frameworks proposed to date to deploy these type of experiments is reduced. In this context, we propose the INVITE computational framework to serve as a useful research tool. The motivation behind the proposed framework is therefore straightforward: to allow researchers to be able to configure without difficulty public goods experiments where they can test their hypothesis regarding the behaviour of individuals, simulate behaviour of automated artificial intelligence and study the interaction between virtual agents and real persons. The greatest advantage of the INVITE framework is that it provides a high level of flexibility in the configuration of game theoretical paradigms. It is possible to configure simple structures such as the 2-player prisoner's dilemma or stag hunt as well as some of the most complex forms of inter-group conflicts such as team games. Accordingly, this framework allows an effortless parametrization, assisted by a tailored configuration tool, of a myriad of public goods games. Moreover, the 3D video game configured in the framework places the players in an immersive and engaging virtual environment where real-life conditions can be replicated and some circumstances, difficult to reproduce in real-life, such as life-threatening situations, can also be simulated. Given its characteristics, the proposed framework aims to represent an important contribution to the study of behaviour of both virtual and human players in scenarios of social conflict.

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Achieving a common goal requires the cooperation of at least some individuals. c This can be modeled by an N-person snowdrift game, a variant of Public Goods Games. c Social networks influence the emerging behavioral dynamics. c Homogeneous networks help the coordination towards stable levels of cooperation. c Heterogeneous networks create a new scenario with multiple behavioral equilibria.

With this research paper, we aim to analyse the effectiveness of Game Theory in Water Resource Allocation through simple twoby-two symmetric water resource games. The results of examples using three kinds of game theory icons are scanned to compare cooperative and non-cooperative game players. The highest payoff is achieved when both the players cooperate hence this paper explains how decision makers’ rational behaviour, who are trying to maximize their own objectives, might result in overall Pareto-inferior outcomes. Keywords— Game Theory, Prisoner’s Dilemma, Chicken game, Stag Hunt

Are public good games really capturing individuals' willingness to contribute to real-life public goods? To answer this question, we conducted a lab-in-the-field experiment with communities who own collective goods. In our experiment, subjects voluntarily contribute to a common pool, which can either be subdivided in individual vouchers, as in standard public good games, or used to acquire collective goods, as it happens for real-life public goods. We show that participants' contributions are larger when the voucher is paid individually, suggesting that individuals' willingness to contribute to public goods may be overestimated when based on results from laboratory experiments.

This paper provides experimental evidence on how players predict end-game effects in a linear public good game. Our regression analysis yields a measure of the relative importance of priors and signals on subjects' beliefs on contributions and allows us to conclude that, first, the weight of the signal is relatively unimportant, while priors have a large weight and, second, priors are the same for all periods. Hence, subjects do not expect end-game effects and there is very little updating of beliefs. We argue that the sustainability of cooperation is related to this pattern of belief formation.

This study investigates the effects of task demonstrability and replacing a human advisor with a machine advisor. Outcome measures include advice-utilization (trust), the perception of advisors, and decision-maker emotions. Participants were randomly assigned to make a series of forecasts dealing with either humanitarian planning (low demonstrability) or management (high demonstrability). Participants received advice from either a machine advisor only, a human advisor only, or their advisor was replaced with the other type of advisor (human/machine) midway through the experiment. Decision-makers rated human advisors as more expert, more useful, and more similar. Perception effects were strongest when a human advisor was replaced by a machine. Decision-makers also experienced more negative emotions, lower reciprocity, and faulted their advisor more for mistakes when a human was replaced by a machine.

Evidence from public goods game experiments holds the promise of informing climate change policies. To fulfill this promise, such evidence needs to demonstrate generalizability to this specific policy context. This paper examines whether and under which conditions behavior in public goods games generalizes to decisions about voluntary climate actions. We observe each participant in two different decision tasks: a real giving task in which contributions are used to directly reduce CO2 emissions and an abstract public goods game. Through treatment variations in this within-subjects design, we explore two factors that are candidates for affecting generalizability: the structural resemblance of contribution incentives between the tasks and the role of the subject pool, students and non-students. Our findings suggest that cooperation in public goods games is only weakly linked to voluntary climate actions and not in a uniform way. For a standard set of parameters, behavior in both tasks ...

Learning the emergence of cooperation in conflicting scenarios such as social dilemmas is a centerpiece of research. Many reinforcement learning based theories exist in the literature to address this problem. The well-known fact about RL based model's very slow learning capabilities coupled with large state space exhibit significant negative effects especially in repeated version of social dilemma settings such as repeated Public Goods Game (PGG) and thereby making them ineffective to model sustainability of cooperation. In this paper, we address this research challenge by augmenting the reinforcement learning based models with a notion of collaboration among the agents, motivated by the fact that humans learn not only through their own actions but also by following the actions of other agents who also continuously learn about the environment. In particular, we propose a novel model, which we refer to as Collaborative Reinforcement Learning (CRL), wherein we define collaboration...

When making financial decisions bank customers are confronted with two types of uncertainty: first, return on investments is uncertain and there is a risk of losing money. Second, customers cannot be certain about their financial advisor’s true intentions. This might decrease customers’ willingness to cooperate with advisors. However, the uncertainty management model and fairness heuristic theory predict that in uncertain situations customers are willing to cooperate with financial advisors when they perceive fairness. In the current study, we investigated how perceived fairness in the twofold uncertain situations increased people’s intended future cooperation with an advisor. We asked customers of financial consultancies about their experienced uncertainty regarding both the investment decision and the advisor’s intentions. Moreover, we asked them about their perceived fairness, as well as their intention to cooperate with the advisor in the future. A three-way moderation analysis ...

It has been pointed that public goods games lack experimental research in topics such as the study of the interaction of groups versus the interaction of individuals and effects of social identity and decision framing. Furthermore, the number of computational frameworks proposed to date to deploy these type of experiments is reduced. In this context, we propose the INVITE computational framework to serve as a useful research tool. The motivation behind the proposed framework is therefore straightforward: to allow researchers to be able to configure without difficulty public goods experiments where they can test their hypothesis regarding the behaviour of individuals, simulate behaviour of automated artificial intelligence and study the interaction between virtual agents and real persons. The greatest advantage of the INVITE framework is that it provides a high level of flexibility in the configuration of game theoretical paradigms. It is possible to configure simple structures such as the 2-player prisoner's dilemma or stag hunt as well as some of the most complex forms of inter-group conflicts such as team games. Accordingly, this framework allows an effortless parametrization, assisted by a tailored configuration tool, of a myriad of public goods games. Moreover, the 3D video game configured in the framework places the players in an immersive and engaging virtual environment where real-life conditions can be replicated and some circumstances, difficult to reproduce in real-life, such as life-threatening situations, can also be simulated. Given its characteristics, the proposed framework aims to represent an important contribution to the study of behaviour of both virtual and human players in scenarios of social conflict.

In Agent-based Modelling and Simulation (ABMS), a system is modelled as a collection of agents, which are autonomous decision-making units with diverse characteristics. The interaction of the individual behaviours of the agents results in the global behaviour of the system. Because ABMS offers a methodology to create an artificial society in which actors with their behaviour can be designed and results of their interaction can be observed, it has gained attention in social sciences such as Economics, Ecology, Social Psychology, and Sociology.

In Economics, ABMS has been used to model many strategic situations. One of the popular strategic situations is the Public Goods Game (PGG). In the PGG, participants secretly choose how many of their private money units to put into a public pot. Social scientists can conduct laboratory experiments of PGGs to study human behaviours in strategic situations. Research findings from these laboratory studies have inspired studies using computational agents and vice versa. However, there is a lack of guidelines regarding the detailed development process and the modelling of agent behaviour for agent-based models of PGGs. We believe that this has contributed to ABMS of PGG not having been used to its full potential.

This thesis aims to leverage the potential of ABMS of PGG, focusing on the development methodology of ABMS and the modelling of agent behaviour. We construct a development framework with incorporated software engineering techniques, then tailored it to ABMS of PGG. The framework uses the Unified Modelling Language (UML) as a standard specification language, and includes a simulation development lifecycle, a step-by-step development guideline, and a short guide for modelling agent behaviour with statecharts. It utilizes software engineering methods to provide a structured approach to identify agent interactions, and design simulation architecture and agent behaviour. The framework is named ABOOMS (Agent-Based Object-Oriented Modelling and Simulation).

After applying the ABOOMS framework to three case studies, the framework demonstrates flexibility in development with two different modelling principles (Keep-It-Simple-Stupid vs. Keep-It-Descriptive-Stupid), capability in supporting complex psychological mechanisms, and ability to model dynamic behaviours in both discrete and continuous time. Additionally, the thesis developed an agent-based model of a PGG in a continuous-time setting. To the best of our knowledge such agent-based models do not exist. During the development, a new social preference, Generous Conditional Cooperators, was introduced to better explain the behavioural dynamics in continuous-time PGG. Experimentation with the agent-based model generated dynamics that are not presented in discrete-time setting. Thus, it is important to study both discrete and continuous time PGG, with laboratory experiment and ABMS. Our new framework allows to do the latter in a structured way.

With the ABOOMS framework, economists can develop PGG simulation models in a structured way and communicate them with a formal model specification. The thesis also showed that there is a need for further investigation on behaviours in continuous-time PGG. For future works, the framework can be tested with variations of PGG or other related strategic interactions.

Agents that interact in complex social situations need to take the social context in consideration in order to perform believably. We argue that social identity is an important factor; therefore, agents should incorporate social identity theory in their behaviour, which implies the ability to categorize others (and themselves) into social groups. In addition , social situations often present social dilemmas with expected rational choices. Social identity may influence the agent to deviate from the rational choice. However, in some situations the rational choice may be the expected, and believable , behaviour. In fact, we argue that finding the dynamics between the social bias induced by social identity and the rational motivation is one of the challenges of building believable agents. In all this, anticipation takes an important role, as it is important to understand the others to cope well with a social situation.

Two reasons why individuals may not contribute toward public goods are because they may be uncertain about both the intentions of others and the value of the public good prior to their contribution. This begs cooperation mechanisms that address both sources of uncertainty simultaneously. We investigate the effect of third-party advice on the estimated value of the public good on cooperation behavior. Experiment 1 finds that the valence of an advisor's estimate affects cooperation behavior, and this advice effect is mediated by the level of trust that an individual has in fellow group members. Experiment 2 shows that when estimates about the value of the public good are mixed that trust in experts declines, and, as a result, trust in other group members also declines. Experiment 3 finds that mixed valence estimates do not affect cooperation behavior when the majority of advisors are in consensus.

The current social dilemma literature lacks theoretical consensus regarding how individuals behave when facing multiple simultaneous social dilemmas. The divided-loyalty hypothesis, from organizational theory, predicts that cooperation will decline as individuals experience multiple social dilemmas with different compared to the same group members. The conditional-cooperation hypothesis, from behavioral economics, predicts that cooperation will increase as individuals experience multiple social dilemmas with different compared to the same group members. We employ a laboratory experiment to create consensus between these literatures and find support for the conditional-cooperation hypothesis. The positive effect of interacting with different group members comes from participants having an opportunity to shift their cooperative behavior from the less cooperative to the more cooperative group.

We investigate how third-party advice on the estimated value of a public good acts as a dual-uncertainty reducing mechanism to encourage cooperation in a trust social dilemma. Experiment 1 finds that the valence of an advisor's estimate affects cooperation behavior and that this advice effect is mediated by the level of trust that an individual has in fellow group members. Experiment 2 finds that when estimates about the value of the public good are mixed, trust in experts declines, and trust in other group members also declines. Experiment 3 finds that mixed valence estimates do not affect cooperation behavior when the majority of advisors are in consensus. In merging the social dilemma and advice-giving literatures, we show one way to navigate the dual-uncertainty problem when producing public goods and how independent, third-party advice can influence interpersonal relations among group members.

This article examined the premises of interpersonal deception theory (IDT) within an advice-giving context. Advisors with quality and persuasion goals provided advice concerning stock rankings to decision makers either primed or not primed to be suspicious of advisors’ motives. Two competing hypotheses were proposed. First, suspicious decision makers were predicted to accept less advice from all advisors and be no more likely to detect advisors’ motives than nonsuspicious decision makers. Second, suspicious decision makers were predicted to be better able to detect the motives of advisors and accept less advice from the advisor with the persuasive motive than nonsuspicious decision makers. The first hypothesis was supported. The persuasive advisor had significantly higher confidence than the quality advisor on the rankings used to give advice, although not on private rankings. Advisors’ confidence on these rankings fully mediated their influence on the decision maker.

This paper analyzes the impact of inequality in the distribution of endowments on contributions. We conduct a lab experiment using the well-known Public Good Game to test the relation between inequality and contribution to a public fund. We introduce the possibility of choosing among three different redistribution rules: equidistribution, proportional to contribution and progressive to endowment. This novelty, combined with a payoff function that depends also on previous period behavior, allows us to verify the hypothesis that players show inequity averse preferences. Results show that inequality has a negative impact on individual contribution. Since inequality decreases during repetitions, we deduce that players show inequity averse preferences.

This paper studies public goods provision in an experiment in which contributors repeatedly interact with rent-extracting administrators. Our main result is that the presence of an administrator reduces contributions but only because rent extraction lowers the MPCR. Analysing the dynamic interactions between the contributors and the administrator, we demonstrate that rent-extraction and cooperation shocks trigger short-run adjustments in the agents’ behaviour. However, shocks do not have permanent effects. This explains the long-run resilience of cooperation to rent extraction. We also show that cooperative attitudes and trust are traits that explain permanent differences in the short-run volatility of public goods provision.

Agent-based Simulation is used for different purposes in Computer Science and Economics. While computer scientists design agents for controlling complex systems or for instilling objects with autonomy and intelligence, economists use agents for gaining a better understanding of the dynamics within economic systems. For the modelling task both communities use their specialised modelling approaches which are very different from each other. We believe that the tools used for the purpose of designing software agents have great potential to be useful for designing agent-based models to study Economics problems. In this paper, we provide some general guidance for building Agent-based Computational Economics (ACE) models using Software Engineering (SE) modelling methods. The applicability of these methods is demonstrated by studying a real-life multi-player dilemma using an ACE and SE approach.

Two reasons why individuals may not contribute toward public goods are because they may be uncertain about both the intentions of others and the value of the public good prior to their contribution. This begs cooperation mechanisms that address both sources of uncertainty simultaneously. We investigate the effect of third-party advice on the estimated value of the public good on cooperation behavior. Experiment 1 finds that the valence of an advisor's estimate affects cooperation behavior, and this advice effect is mediated by the level of trust that an individual has in fellow group members. Experiment 2 shows that when estimates about the value of the public good are mixed that trust in experts declines, and, as a result, trust in other group members also declines. Experiment 3 finds that mixed valence estimates do not affect cooperation behavior when the majority of advisors are in consensus.