Finite Neighborhood Binary Games: a Structural Study

The purpose of this study is to present a systematic analysis of the long-term behavior of the agents of an artificial society under varying payoff functions in finite neighborhood binary games. By assuming the linearity of the payoffs of both cooperating and defecting agents, the type of the game is determined by four fundamental parameters. By fixing the values of three of them and systematically varying the fourth one we can observe a transition from Prisoner\'s Dilemma to Leader Game through Chicken and Benevolent Chicken Games. By using agent-based simulation we are able to observe the long-term behavior of the artificial society with different and gradually changing payoff structure. The difference between different games is explored and the effect of the transition from one game to the other on the society is investigated. The results depend on the personality types of the agents. In this study greedy and Pavlovian agents are considered. In the first case, we observe the most significant change in trajectory structure between Prisoner\'s Dilemma and Chicken Games showing significant difference in the behavioral patterns of the agents. Almost no changes can be observed between Benevolent Chicken and Leader Games, and only small change between Chicken and Benevolent Chicken. The trajectories change from always converging to regularly oscillating patterns with systematically altering amplitude and central values. The results are very similar whether the agents consider themselves as members of their neighborhoods or not. With Pavlovian agents no significant difference can be observed between the four games, the trajectories always converge and the limits smoothly and monotonically depend on the value of the varying parameter.Agent-Based Simulation, N-Person Games, Structure Analysis, Equilibrium

Associating sporadic, foodborne illness caused by Shiga toxin-producing Escherichia coli with specific foods : a systematic review and meta-analysis of case-control studies

Shiga toxin-producing Escherichia coli (STEC) infections are a significant public health issue, with foodborne transmission causing >1 million illnesses worldwide each year. We conducted a systematic review and meta-analysis (PROSPERO registry # CRD42017074239), to determine the relative association of different food types with sporadic illnesses caused by STEC. Searches were conducted from 01 August to 30 September 2017, using bibliographic and grey literature databases, websites and expert consultation. We identified 22 case-control studies of sporadic STEC infection in humans, from 10 countries within four World Health Organization subregions, from 1985 to 2012. We extracted data from 21 studies, for 237 individual measures in 11 food categories and across three status types (raw or undercooked, not raw and unknown). Beef was the most significant food item associated with STEC illness in the Americas and Europe, but in the Western Pacific region, chicken was most significant. These findings were not significantly moderated by the raw or cooked status of the food item, nor the publication year of the study. Data from the African, South-East Asian and Eastern Mediterranean subregions were lacking and it is unclear whether our results are relevant to these regions

Progress in Behavioral Game Theory

Is game theory meant to describe actual choices by people and institutions or not? It is remarkable how much game theory has been done while largely ignoring this question. The seminal book by von Neumann and Morgenstern, The Theory of Games and Economic Behavior, was clearly about how rational players would play against others they knew were rational. In more recent work, game theorists are not always explicit about what they aim to describe or advise. At one extreme, highly mathematical analyses have proposed rationality requirements that people and firms are probably not smart enough to satisfy in everyday decisions. At the other extreme, adaptive and evolutionary approaches use very simple models-mostly developed to describe nonhuman animals-in which players may not realize they are playing a game at all. When game theory does aim to describe behavior, it often proceeds with a disturbingly low ratio of careful observation to theorizing

Bargaining and Influence in Conflict Situations

[Excerpt] This chapter examines bargaining as an influence process through which actors attempt to resolve a social conflict. Conflict occurs when two or more interdependent actors have incompatible preferences and perceive or anticipate resistance from each other (Blalock 1989; Kriesberg 1982). Bargaining is a basic form of goal-directed action that involves both intentions to influence and efforts by each actor to carry out these intentions. Tactics are verbal and/or nonverbal actions designed to maneuver oneself into a favorable position vis-a-vis another or to reach some accommodation. Our treatment of bargaining subsumes the concept of negotiation (see Morley and Stephenson 1977). This chapter is organized around a conceptual framework that distinguishes basic types of bargaining contexts. We begin by introducing the framework and then present an overview of and analyze theoretical and empirical work on each type of bargaining context

A Model of Noisy Introspection

This paper presents a theoretical model of noisy introspection designed to explain behavior in games played only once. The equilibrium determines layers of beliefs about others' beliefs about ..., etc., but allows for surprises by relaxing the equilibrium requirement that belief distributions coincide with decision distributions. The paper contains a convergence proof and reports estimated introspection and error parameters for data from 37 one-shot matrix games. The accuracy of the model is compared with that of two alternative approaches: the Nash equilibrium and the logit quantal response equilibrium.game theory, introspection, Nash equilibrium, experiments.

Dynamics of growth factor production in monolayers of cancer cells and evolution of resistance to anticancer therapies

Tumor heterogeneity is well documented for many characters, including the production of growth factors, which improve tumor proliferation and promote resistance against apoptosis and against immune reaction. What maintains heterogeneity remains an open question that has implications for diagnosis and treatment. While it has been suggested that therapies targeting growth factors are robust against evolved resistance, current therapies against growth factors, like antiangiogenic drugs, are not effective in the long term, as resistant mutants can evolve and lead to relapse. We use evolutionary game theory to study the dynamics of the production of growth factors by monolayers of cancer cells and to understand the effect of therapies that target growth factors. The dynamics depend on the production cost of the growth factor, on its diffusion range and on the type of benefit it confers to the cells. Stable heterogeneity is a typical outcome of the dynamics, while a pure equilibrium of nonproducer cells is possible under certain conditions. Such pure equilibrium can be the goal of new anticancer therapies. We show that current therapies, instead, can be effective only if growth factors are almost completely eliminated and if the reduction is almost immediate

Learning in Repeated Games: Human Versus Machine

While Artificial Intelligence has successfully outperformed humans in complex combinatorial games (such as chess and checkers), humans have retained their supremacy in social interactions that require intuition and adaptation, such as cooperation and coordination games. Despite significant advances in learning algorithms, most algorithms adapt at times scales which are not relevant for interactions with humans, and therefore the advances in AI on this front have remained of a more theoretical nature. This has also hindered the experimental evaluation of how these algorithms perform against humans, as the length of experiments needed to evaluate them is beyond what humans are reasonably expected to endure (max 100 repetitions). This scenario is rapidly changing, as recent algorithms are able to converge to their functional regimes in shorter time-scales. Additionally, this shift opens up possibilities for experimental investigation: where do humans stand compared with these new algorithms? We evaluate humans experimentally against a representative element of these fast-converging algorithms. Our results indicate that the performance of at least one of these algorithms is comparable to, and even exceeds, the performance of people

Tacit Coordination in a Decentralized Market Entry Game with Fixed Capacity

Tacit coordination is studied experimentally in a class of iterated market entry games with a relatively small number of potential entrants (n = 6), symmetric players, and fixed entry fees. These games are intended to simulate a situation where a newly emergent market opportunity may be fruitfully exploited by no more than a fixed and commonly known number of firms. Our results indicate a high degree of sensitivity to the game parameters that are manipulated in the study, namely, the market capacity, entry fee, and method of subject assignment to groups (fixed vs. random), as well as sophisticated adaptation to actual and hypothetical changes in wealth level. We find no support for convergence to equilibrium play on either the aggregate or individual level or for any trend across rounds of play to maximize total group payoff by lowering the frequency of entry. The coordination failure is attributed to certain features of the payoff function that induce strong competition in the attempt to penetrate the market.Tacit Coordination, Market Entry Game, Experiment