Behavioral Game Theory Research Articles

Behavioural strategies in simultaneous and alternating prisoner’s dilemma games with/without voluntary participation

The Prisoner’s Dilemma is one of the most classic formats for exploring the principle of direct reciprocity. Although numerous theoretical and experimental studies have been conducted, little attention has been paid to the divergence between theoretical predictions and actual human behaviour. In addition, there are two additional essential challenges of experimental research. First, most experimental approaches have focused on games in which two players decide their actions simultaneously, but little is known about alternating games. Another is that there are few experiments on voluntary participation. Here, we conducted experiments on simultaneous games, alternating games, and games with and without voluntary participation for a total of four game patterns and examined the deviation from theoretical predictions for each. The results showed that, contrary to theoretical predictions, humans chose cooperation even after being exploited. We also observed that, with or without voluntary participation, people tended to take the same action they had taken in the previous round. Our results indicate that to understand the mechanisms of human behaviour, we need to integrate findings from behavioural science, psychology, and game theory.

The collective wisdom of behavioral game theory

AbstractWe apply an algorithm from the wisdom-of-crowds literature to optimally combine behavioral game theory models to more accurately predict strategic choice in one-shot, simultaneous-move games. We find that the optimal weighted average of seven behavioral game theory models predicts out-of-sample choice behavior significantly better than any of the individual models. The crowd of behavioral game theory models is wiser than any single one of them. Different strategic choice models complement each other by capturing distinct patterns of behavior. The field of behavioral game theory is enriched by having this diversity of models.

Socio-legal basis of criminal liability of legal persons and corporate compliance to prevent crimes: Anthropic approach and behavioral game theory

Systems theory has garnered significant criticism from internationally recognized experts in the field of organizational analysis. However, a sector of criminal doctrine is adopting a strict approach to this theory to justify socio-legally the application of corporate criminal liability (this is a trend that has been embraced by the Spanish Supreme Court and is spreading throughout Latin America). The systemic idea of organization excludes the individuals who constitute and manage the company, dehumanizing it, separating corporate governance from the role played by individuals and even attributing human attributes to the company. In other words, the members that make up the organization, as well as the influences or constraints they generate in the context of the interaction of individuals within a corporation, are disregarded in the analysis. As an alternative, this text proposes the essential outlines of an "anthropic model" of corporate criminal liability, which is built on theoretically and experimentally validated notions and methodologies: neo-institutionalism, game theory and behavioral compliance. In this anthropic model, the adoption and implementation of an effective and adequate governance system to prevent irregularities (or crimes) does not depend on the system itself, but depends directly on the commitment and implementation of the "human component" of the organization. La teoría de sistemas ha recibido críticas significativas por parte de expertos internacionalmente reconocidos en el campo del análisis de las organizaciones. Sin embargo, cierto sector de la doctrina penal está adoptando un enfoque estricto de esta teoría para justificar sociojurídicamente la aplicación de la responsabilidad penal de las empresas (una tendencia que ha sido aceptada por el Tribunal Supremo español y se está extendiendo por América Latina). La idea sistémica de la organización excluye del análisis a los individuos que constituyen y gestionan la empresa, deshumanizándola, separando la gobernanza corporativa del papel desempeñado por los individuos e incluso atribuyendo atributos humanos a la empresa. En otras palabras, se desestiman en el análisis penal tanto a los miembros que conforman la organización como a las influencias o constricciones que originan en ese contexto de interacción de individuos que configura la corporación. Como alternativa, se proponen los lineamientos esenciales de un “modelo antrópico” de responsabilidad penal corporativa, construido sobre nociones y metodologías teórica y experimentalmente validadas: neoinstitucionalismo, teoría de juegos y “behavioral compliance”. En este modelo antrópico, la adopción e implementación de un sistema de gobernanza efectivo y adecuado para prevenir irregularidades (o delitos) no depende del sistema en sí, sino que pende directamente del compromiso e implementación efectiva por parte del “componente humano” de la organización.

The role of social capital in community development: Insights from behavioral game theory and social network analysis

AbstractThis study examined the multifaceted nature of Social Capital (SC) in community development projects by using Water, Sanitation, and Hygiene (WASH) interventions in three urban Bangladeshi slums as a case study. Despite its recognized importance, the ambiguity surrounding SC has led to mixed outcomes, necessitating a more theorized approach to quantitatively assess SC. To address this, a comprehensive methodology was developed, specifically crafted for a community development context and incorporating standard SC questions, social network analysis indices, and an adherence to social norms through behavioral game theory. The analysis revealed varied SC profiles, with one slum achieving successful community toilet management through strong reciprocal ties. The other slums faced challenges due to community division and weakened SC among key individuals. These findings underscore the necessity of tailoring interventions to community‐specific SC characteristics. The streamlined approach derived from the study provides guidance for both enhancing the effectiveness of community interventions and advancing sustainable development.

Quantal Response Equilibrium and Rationalizability: Inside the Black Box

This paper aims to connect epistemic and behavioral game theory by examining the epistemic foundations of quantal response equilibrium (QRE) in static games. We focus on how much information agents possess about the probability distributions of idiosyncratic payoff shocks, in addition to the standard assumptions of rationality and common belief in rationality. When these distributions are transparent, we obtain a solution concept called Δp-rationalizability, which includes action distributions derived from QRE; we also give a condition under which this relationship holds true in reverse. When agents only have common belief in the monotonicity of these distributions (for example, extreme value distributions), we obtain another solution concept called ΔM-rationalizability, which includes action distributions derived from rank-dependent choice equilibrium, a parameter-free variant of QRE. Our solution concepts also provide insights for interpreting experimental and empirical data.

Location-pricing problem in a two-echelon supply chain: A behavioral game-theoretic approach

The location-pricing problem is a relatively new class of problems in operations research that aims to solve both the pricing and facility location problems simultaneously. Unlike the classical facility location problem, where the demand of customers is known, in the location-pricing problem, the demand is a function of price. Other factors can also influence the final price of products, including production, transportation and inventory costs, competition and agents' behavior, which make the location-pricing problem complex. To this end, this paper proposes a bi-level mixed integer nonlinear programming model for the location-pricing problem in a two-echelon supply chain with players who make decisions based on their social preferences in a competitive market. The social preferences of players are a topic of great importance in the field of behavioral game theory. If the supply chain players care about the profits of other competitors in their utility functions, the simplifying assumption of pure selfishness must be corrected. It is predicted that considering social preferences for supply chain members can lead to better outcomes and payoffs. This paper proposes a single-to-multiple Stackelberg-Nash game in which a focal company serves as the leader, responsible for the location, capacity allocation, pricing, and production/distribution planning of manufacturers. Additionally, there are two competing retailers, as followers who base their pricing strategies on social preferences in order to gain a larger market share. The competition between the retailers determines the demands of customers. The effects of various parameters are then examined using a sensitivity analysis, and some economic and managerial insights are provided. The findings indicate that when both retailers exhibit status-seeking behavior in the market, average market demand increases as a result of lower retail and wholesale prices, resulting in an increase in the profit margins of the supply chain and agents compared to a model with self-interested players.

Designing a social policy initiative for greater cooperation: social dilemmas, behavioural game theory, motives, altruism and ethics

Drawing on the experience of editing this special issue, I propose a method for developing the early stages of social policy interventions requiring cooperation, based on two phenomena foundational to approaching this challenge. I recommend that the first stage is methodological – the application of behavioural game theory – and that the second is analysis of a psychological process – the motivations of those involved. A potential third step is use of a toolbox of factors known to encourage cooperation that I discussed in my introductory article for the issue. Three further processes are important for social dilemma policy development: conditional cooperation, trust and feedback. I go on to discuss: the contrasting properties of selfish and altruistic motives for cooperating, particularly in terms of their sensitivity to influence; the long-term prospects for altruistically motivated cooperation; and ethical aspects of tackling societal dilemmas through bottom-up and top-down agents for change. Finally, I consider the current state of the relationship between behavioural science and social policy.

Behavioural game theory perspectives on cooperation: a reply to ‘Nurturing, nudging and navigating the increasingly precarious nature of cooperation in public health: the cases of vaccination and organ donation’ by Heidi J. Larson and Alexander H. Toledo

Behavioural game theory perspectives on cooperation: a reply to ‘Nurturing, nudging and navigating the increasingly precarious nature of cooperation in public health: the cases of vaccination and organ donation’ by Heidi J. Larson and Alexander H. Toledo

The Diffusion of Competitive Platform-Based Products with Network Effects

The existence of network effects not only changes traditional product diffusion patterns but also has significant impacts on individuals’ decision behaviors. Previous studies on competitive product diffusion have focused on macro-level diffusion speed and effects while neglecting the micro-level impacts of individual heterogeneity and social interaction on product diffusion. This paper introduces the individual heterogeneities and social interactions of consumers into the competitive product diffusion model on the basis of a two-sided market framework and complex network theory. We proposed a small world network model and behavioral game theory. Specifically, the small world network model was used to build interactions between users, and behavioral game theory was utilized to describe the interactions between users. In our model, the direct network effect was distinguished from the indirect network effect on the basis of the synergy of multiple complementary products. The results show that the final distribution often presented a situation wherein two products coexisted, but the market share was very different. In an asymmetric first-mover situation, the direct network effect dominated the indirect network effect. Moreover, the dominant position of one product at present can be changed by the other under certain conditions. Finally, when the switching and learning costs were high, the market maintained its concentration, and the prior platform was unable to dominate the market. A decrease in the costs raised the prior platform’s market share and the speed of market occupation.

Explaining human interactions on the road by large-scale integration of computational psychological theory.

When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others' intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions-but not simpler versions of the same model-can account for a number of previously unexplained phenomena in naturalistic driver-pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

The “Black Box” Method for Experimental Economics

How humans behave in repeated strategic interactions, how they learn, how their decisions adapt, and how their decision-making evolves is a topic of fundamental interest in behavioral economics and behavioral game theory [...]

A framework to design game theory-based interventions for strategic analysis of real-world problems with stakeholders

Game theory provides a valuable conceptual framework for developing strategic insight into complex, multi-actor situations and is well-suited for addressing real-world problems. Participatory game theory involves model construction and analysis with stakeholders guided by a facilitator with the aim of examining strategic interactions within real-world problems. Previous work in this field focused often on the analytical insights from game theory models whereas guidance on the design and implementation process of participatory game theory interventions remains under researched. This paper addresses this gap by providing a framework to assist in the design of game theory-based interventions. Here, stakeholders’ decision-support needs are designed according to specific learning levels, as defined by Bloom's taxonomy and triple-loop learning, that the intervention will target. The framework further specifies the design choices for each learning level by comparing the design choices from two case studies of game theory-based interventions in Dutch railway and Bangladesh drinking water contexts. The former involved stakeholders using a game concept identification tool while the latter used game theory-based role-playing games with community and local government stakeholders. This paper describes the value of this framework for structured learning through game theory-based interventions. It includes the facilitator's role and design choices for each learning level related to the knowledge capabilities and activity focus to achieve that learning outcome. Future work will incorporate design inputs from additional learning theories, behavioural game theory and will explore the framework's potential for evaluating game theory-based interventions.

Attack Identification for Cyber-Physical Security in Dynamic Games under Cognitive Hierarchy

This paper considers the problem of identifying the profiles and capabilities of attackers injecting adversarial inputs to a cyber-physical system. The system in question interacts with attackers of different levels of intelligence, each employing different feedback controllers against the system. Principles of behavioral game theory – specifically the concept of level-k thinking – is employed to construct a database of potential attack vectors. By observing the state trajectories under sequential interactions with different adversaries, the defender adaptively estimates both the number and profiles of the different attack signals using an online deterministic annealing approach. This information is used to dynamically estimate the level of intelligence of the attackers. Simulation results showcase the efficacy of the proposed method.

Model selection accuracy in behavioral game theory: A simulation

We simulate a horse race between several behavioral models of play in one-shot games. First, we find that many models can lead to identical predictions, making it impossible to select a unique winning model. This is largely avoided by comparing only two models. But even then we find that cross-validation sometimes fails to select the true model, often because models are be estimated to be noiseless but then fail to predict out-of-sample data. The Bayesian Information Criterion avoids this problem, though the inflexibility of its parameter penalty appears to cause poor performance in certain settings.

Disentangling leaf-microbiome interactions in Arabidopsis thaliana by network mapping.

The leaf microbiota plays a key role in plant development, but a detailed mechanism of microbe-plant relationships remains elusive. Many genome-wide association studies (GWAS) have begun to map leaf microbes, but few have systematically characterized the genetics of how microbes act and interact. Previously, we integrated behavioral ecology and game theory to define four types of microbial interactions – mutualism, antagonism, aggression, and altruism, in a microbial community assembly. Here, we apply network mapping to identify specific plant genes that mediate the topological architecture of microbial networks. Analyzing leaf microbiome data from an Arabidopsis GWAS, we identify several heritable hub microbes for leaf microbial communities and detect 140–728 SNPs (Single nucleotide polymorphisms) responsible for emergent properties of microbial network. We reconstruct Bayesian genetic networks from which to identify 22–43 hub genes found to code molecular pathways related to leaf growth, abiotic stress responses, disease resistance and nutrition uptake. A further path analysis visualizes how genetic variants of Arabidopsis affect its fecundity through the internal workings of the leaf microbiome. We find that microbial networks and their genetic control vary along spatiotemporal gradients. Our study provides a new avenue to reveal the “endophenotype” role of microbial networks in linking genotype to end-point phenotypes in plants. Our integrative theory model provides a powerful tool to understand the mechanistic basis of structural-functional relationships within the leaf microbiome and supports the need for future research on plant breeding and synthetic microbial consortia with a specific function.

Creating the Conditions for Intergenerational Justice: Social Capital and Compliance

Creating the Conditions for Intergenerational Justice: Social Capital and Compliance

Analyzing the relation between anchoring and dark triad personality traits in the prisoner’s and traveler’s dilemmas

Anchoring bias has been extensively studied in behavioral economics, but the influence of individual differences on this bias has rarely received attention. However, research suggests that personality traits may affect susceptibility to bias. The present study addresses how dark triad traits may affect decisions within the framework of behavioral game theory, how decisions are justified, whether anchors are generated, and how personality traits affect anchors. A total of 379 participants played the prisoner’s dilemma and traveler’s dilemma games. In both games, three groups were configured, two with anchors and one control group without anchors. The games were conducted through an online questionnaire in which participants also completed the short dark triad test. The Machiavellianism and psychopathy traits yielded relationships with the decisions made in the games but did not affect the anchors. In the traveler’s dilemma, there was a clear anchoring, and in the prisoner’s dilemma, there was no anchoring. In addition, the players rationalized the decisions made against anchoring.

Gambits: Theory and evidence

AbstractGambits are central to human decision‐making. Our goal is to provide a theory of Gambits. A Gambit is a combination of psychological and technical factors designed to disrupt predictable play. Chess provides an environment to study gambits and behavioral game theory. Our theory is based on the Bellman optimality path for sequential decision‐making. This allows us to calculate the ‐values of a Gambit where material (usually a pawn) is sacrificed for dynamic play. On the empirical side, we study the effectiveness of a number of popular chess Gambits. This is a natural setting as chess Gambits require a sequential assessment of a set of moves (a.k.a. policy) after the Gambit has been accepted. Our analysis uses Stockfish 14.1 to calculate the optimal Bellman ‐values, which fundamentally measures if a position is winning or losing. To test whether Bellman's equation holds in play, we estimate the transition probabilities to the next board state via a database of expert human play. This then allows us to test whether the Gambiteer is following the optimal path in his decision‐making. Our methodology is applied to the popular Stafford and reverse Stafford (a.k.a. Boden–Kieretsky–Morphy) Gambit and other common ones including the Smith–Morra, Goring, Danish and Halloween Gambits. We build on research in human decision‐making by proving an irrational skewness preference within agents in chess. We conclude with directions for future research.

Behavioral Game Theory Model in Pollution Control with Additional Supervision

The work is devoted to the study of the impact of external control on the strategies of pollutant discharge enterprises and government regulators in the field of environmental protection. The authors construct a model of the relationship between these entities. It is an evolutionary game in which the players are entities that generate pollutants and the government departments that implement pollution supervision. The choice of strategies of both of these entities and the evolutionary stability of the system controlled by different regulatory efforts, i.e., a third party, are analyzed. The authors then verify the evolutionary paths and evolutionary results of the model under different conditions using simulation analysis based on this model. The conducted research shows that the weak power of third-party supervision is not enough to promote the evolution of the behavioral decisions of the government and enterprises. An appropriate increase in the power of third-party supervision will change the choice of the government and enterprises strategies in the short term; however, due to the mutual influence of the strategies between both sides of the game, in this situation, the evolutionary system does not pursue a stable state. The strong power of third-party supervision will push enterprises to choose a pollution control strategy, change the intensity of government supervision, and replace government supervision to a certain extent. It is an interesting example of modeling the relationship of this system on the basis of evolutionary game theory. The findings can be regarded as a theoretical reference for environmental pollution control of enterprises.

Model Selection Accuracy in Behavioral Game Theory: A Simulation

Model Selection Accuracy in Behavioral Game Theory: A Simulation