We study an evolutionary game of chance in which the probabilities for different outcomes (e.g., heads or tails) depend on the amount wagered on those outcomes. The game is perhaps the simplest possible probabilistic game in which perception affects reality. By varying the reality map', which relates the amount wagered to the probability of the outcome, it is possible to move continuously from a purely objective game in which probabilities have no dependence on wagers, to a purely subjective game in which probabilities equal the amount wagered. The reality map can reflect self-reinforcing strategies or self-defeating strategies. In self-reinforcing games, rational players can achieve increasing returns and manipulate the outcome probabilities to their advantage; consequently, an early lead in the game, whether acquired by chance or by strategy, typically gives a persistent advantage. We investigate the game both in and out of equilibrium and with and without rational players. We introduce a method of measuring the inefficiency of the game and show that in the large time limit the inefficiency decreases slowly in its approach to equilibrium as a power law with an exponent between zero and one, depending on the subjectivity of the game.
There has been considerable past work on situations where subjective factors influence objective outcomes. Some examples include Hommes's studies of cobweb models @cite_8 @cite_3 , studies of increasing returns @cite_1 , Arthur's El Farol model and its close relative the minority game @cite_2 @cite_9 , Blume and Easley's model of the influence of capital markets on natural selection in an economy @cite_5 @cite_12 , and Akiyama and Kaneko's example of a game that changes due to the players' behaviors and states @cite_11 . The model we introduce here has the advantage of being very general yet very simple, providing a tunable way to study this phenomenon under varying levels of feedback.
@cite_8 @cite_9 @cite_1 @cite_3 @cite_2 @cite_5 @cite_12 @cite_11
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We study a setting where a set of players simultaneously invest in a shared resource. The resource has a probability of failure and a return on investment, both of which are functions of the total investment by all players. We use a simple reference dependent preference model to capture players with heterogeneous risk attitudes (risk seeking, risk neutral and risk averse). We show the existence and uniqueness of a pure strategy Nash equilibrium in this setting and examine the effect of different risk attitudes on players' strategies in the presence of uncertainty. In particular, we show that at the equilibrium, risk averse players are pushed out of the resource by risk seeking players. We compare the failure probabilities in the decentralized (game-theoretic) and centralized settings, and show that our proposed game belongs to the class of best response potential games, for which there are simple dynamics that allow all players to converge to the equilibrium. Studies in experimental economics have consistently demonstrated that Nash equilibrium is a poor description of human players' behavior in unrepeated normal-form games. Behavioral game theory offers alternative models that more accurately describe human behavior in these settings. These models typically depend upon the values of exogenous parameters, which are estimated based on experimental data. We describe methods for deriving and analyzing the posterior distributions over the parameters of such models, and apply these techniques to study two popular models (Poisson-CH and QLk), the latter of which we previously showed to be the best-performing existing model in a comparison of four widely-studied behavioral models [22]. Drawing on a large set of publicly available experimental data, we derive concrete recommendations for the parameters that should be used with Poisson-CH, contradicting previous recommendations in the literature. We also uncover anomalies in QLk that lead us to develop a new, simpler, and better-performing family of models. We consider a task of scheduling with a common deadline on a single machine. Every player reports to a scheduler the length of his job and the scheduler needs to finish as many jobs as possible by the deadline. For this simple problem, there is a truthful mechanism that achieves maximum welfare in dominant strategies. The new aspect of our work is that in our setting players are uncertain about their own job lengths, and hence are incapable of providing truthful reports (in the strict sense of the word). For a probabilistic model for uncertainty we show that even with relatively little uncertainty, no mechanism can guarantee a constant fraction of the maximum welfare. To remedy this situation, we introduce a new measure of economic efficiency, based on a notion of a fair share of a player, and design mechanisms that are Ω(1)-fair. In addition to its intrinsic appeal, our notion of fairness implies good approximation of maximum welfare in several cases of interest. In our mechanisms the machine is sometimes left idle even though there are jobs that want to use it. We show that this unfavorable aspect is unavoidable, unless one gives up other favorable aspects (e.g., give up Ω(1)-fairness). We also consider a qualitative approach to uncertainty as an alternative to the probabilistic quantitative model. In the qualitative approach we break away from solution concepts such as dominant strategies (they are no longer well defined), and instead suggest an axiomatic approach, which amounts to listing desirable properties for mechanisms. We provide a mechanism that satisfies these properties. Many natural games have both high and low cost Nash equilibria: their Price of Anarchy is high and yet their Price of Stability is low. In such cases, one could hope to move behavior from a high cost equilibrium to a low cost one by a "public service advertising campaign" encouraging players to follow the low-cost equilibrium, and if every player follows the advice then we are done. However, the assumption that everyone follows instructions is unrealistic. A more natural assumption is that some players will follow them, while other players will not. In this paper we consider the question of to what extent can such an advertising campaign cause behavior to switch from a bad equilibrium to a good one even if only a fraction of people actually follow the given advice, and do so only temporarily. Unlike the "value of altruism" model, we assume everyone will ultimately act in their own interest. We analyze this question for several important and widely studied classes of games including network design with fair cost sharing, scheduling with unrelated machines, and party affiliation games (which include consensus and cut games). We show that for some of these games (such as fair cost sharing), a random α fraction of the population following the given advice is sufficient to get a guarantee within an O(1 α) factor of the price of stability for any α > 0. For other games (such as party affiliation games), there is a strict threshold (in this case, α 1 2 is enough to reach near-optimal behavior). Finally, for some games, such as scheduling, no value α < 1 is sufficient. We also consider a "viral marketing" model in which certain players are specifically targeted, and analyze the ability of such targeting to influence behavior using a much smaller number of targeted players.