Persistence in repeated games encourages the evolution of spite.

Social behavior is divided into four types: altruism, spite, mutualism, and selfishness. The former two are costly to the actor; therefore, from the perspective of natural selection, their existence can be regarded as mysterious. One potential setup which encourages the evolution of altruism and spite is repeated interaction. Players can behave conditionally based on their opponent's previous actions in the repeated interaction. On the one hand, the retaliatory strategy (who behaves altruistically when their opponent behaved altruistically and behaves non-altruistically when the opponent player behaved non-altruistically) is likely to evolve when players choose altruistic or selfish behavior in each round. On the other hand, the anti-retaliatory strategy (who is spiteful when the opponent was not spiteful and is not spiteful when the opponent player was spiteful) is likely to evolve when players opt for spiteful or mutualistic behavior in each round. These successful conditional behaviors can be favored by natural selection. Here, we notice that information on opponent players' actions is not always available. When there is no such information, players cannot determine their behavior according to their opponent's action. By investigating the case of altruism, a previous study (Kurokawa, 2017, Mathematical Biosciences, 286, 94-103) found that persistent altruistic strategies, which choose the same action as the own previous action, are favored by natural selection. How, then, should a spiteful conditional strategy behave when the player does not know what their opponent did? By studying the repeated game, we find that persistent spiteful strategies, which choose the same action as the own previous action, are favored by natural selection. Altruism and spite differ concerning whether retaliatory or anti-retaliatory strategies are favored by natural selection; however, they are identical concerning whether persistent strategies are favored by natural selection.

Fuzzy-Based Privacy-Preserving Scheme of Low Consumption and High Effectiveness for IoTs: A Repeated Game Model.

In the Internet of things (IoTs), data transmission via network coding is highly vulnerable to intra-generation and inter-generation pollution attacks. To mitigate such attacks, some resource-intensive privacy-preserving schemes have been adopted in the previous literature. In order to balance resource consumption and data-privacy-preserving issues, a novel fuzzy-based privacy-preserving scheme is proposed. Our scheme is constructed on a T-S fuzzy trust theory, and network coding data streams are routed in optimal clusters formulated by a designed repeated game model to defend against pollution attacks. In particular, the security of our scheme relies on the hardness of the discrete logarithm. Then, we prove that the designed repeated game model has a subgame-perfect Nash equilibrium, and the model can improve resource utilization efficiency under the condition of data security. Simulation results show that the running time of the proposed privacy-preserving scheme is less than 1 s and the remaining energy is higher than 4 J when the length of packets is greater than 400 and the number of iterations is 100. Therefore, our scheme has higher time and energy efficiency than those of previous studies. In addition, the effective trust cluster formulation scheme (ETCFS) can formulate an optimal cluster more quickly under a kind of camouflage attack.

The emergence of conventions in the repeated volunteer's dilemma: The role of social value orientation, payoff asymmetries and focal points.

Conventions are arbitrary rules of behavior that coordinate social interactions. Here we study the effects of individuals' social value orientations (SVO) and situational conditions on the emergence of conventions in the three-person volunteer's dilemma (VOD). The VOD is a step-level collective good game in which only one actor's action is required to produce a benefit for the group. It has been shown that if actors interact in the payoff-symmetric VOD repeatedly, a turn-taking convention emerges, resulting in an equal distribution of payoffs. If the VOD is asymmetric, with one "strong" actor having lower costs of volunteering, a solitary-volunteering convention emerges by which the strong actor volunteers earning less than others. In study 1 we test whether SVO promotes turn-taking and hampers solitary-volunteering. We find that groups with more prosocials engage less in turn-taking and no effect of SVO on the emergence of solitary-volunteering. In study 2 we test whether making one actor focal is sufficient for solitary-volunteering to emerge. We find instead that payoff asymmetry with one strong actor is a necessary precondition. We discuss explanations for our findings and propose directions for future research.

Repeated Game Analysis of a CSMA/CA Network under a Backoff Attack.

We study a CSMA/CA (Carrier Sense Medium Access with Collision Avoidance) wireless network where some stations deviate from the defined contention mechanism. By using Bianchi's model, we study how this deviation impacts the network throughput and show that the fairness of the network is seriously affected, as the stations that deviate achieve a larger share of the resources than the rest of stations. Previously, we modeled this situation using a static game and now, we use repeated games, which, by means of the Folk theorem, allow all players to have better outcomes. We provide analytical solutions to this game for the two player case using subgame perfect and correlated equilibria concepts. We also propose a distributed algorithm based on communicating candidate equilibrium points for learning the equilibria of this game for an arbitrary number of players. We validate approach using numerical simulations, which allows comparing the solutions we propose and discussing the advantages of using each of the methods we propose.

Analyzing reciprocity dynamics in supply chains of public goods: a stochastic evolutionary game approach.

To start with an infinitely repeated game of supply chains of public goods, a stout reciprocity mechanism is introduced into income games to build a matric dynamic equation. The conventional evolutionary game method is employed to propose a model called the evolutionary game for the cooperative strategy of both the manufacturer and the seller groups in the supply chain of public goods. Also, white Gaussian noise (WGN) is added to reflect random interference in the evolution process. Then, a stochastic dynamic system is established, and Ito's differential equation is used to analyze both sides' strategy evolution in a game, interpret changes in system stability when random disturbance is added, and finally test the influence of different situations on the system stability by running a numerical simulation. The research shows that the stronger the reciprocity coefficient is, and the system is subjected to random interference, the greater the strategy choice change in players' decision-making procedures when the repeated game of public goods is conducted.

Worker-firm relational contracts in the time of shutdowns: experimental evidence.

Exogeneous disruptions in labor demand have become more frequent in recent times. The COVID-19 pandemic has resulted in millions of workers being repeatedly laid off and rehired according to local public health conditions. This may be bad news for market efficiency. Typical employment relations-which resemble non-enforceable (implicit) contracts-rely on reciprocity (Brown et al. in Econometrica 72:747-780, 2004), and hence could be harmed when workers' efforts no longer guarantee reemployment in the next period. In this paper we extend the BFF paradigm to include a per-period probability (0%, 10%, 50%) of publicly observable "shutdown", where a specific firm cannot contract with any workers for several periods. A Perfect Bayesian Equilibrium exists in which these shutdowns destabilize relationships, but do not harm efficiency. Our experiment shows that, remarkably, market efficiency can be maintained even with very frequent stochastic shutdowns. However, the dynamic of relational contracts changes from one where a worker finds stable employment to one where she juggles multiple employers, laying the burden of maintaining productivity upon workers and worsening worker-side inequality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1007/s10683-020-09697-1).

A resource-sharing model based on a repeated game in fog computing.

With the rapid development of cloud computing techniques, the number of users is undergoing exponential growth. It is difficult for traditional data centers to perform many tasks in real time because of the limited bandwidth of resources. The concept of fog computing is proposed to support traditional cloud computing and to provide cloud services. In fog computing, the resource pool is composed of sporadic distributed resources that are more flexible and movable than a traditional data center. In this paper, we propose a fog computing structure and present a crowd-funding algorithm to integrate spare resources in the network. Furthermore, to encourage more resource owners to share their resources with the resource pool and to supervise the resource supporters as they actively perform their tasks, we propose an incentive mechanism in our algorithm. Simulation results show that our proposed incentive mechanism can effectively reduce the SLA violation rate and accelerate the completion of tasks.

Transparency and cooperation in repeated dilemma games: a meta study.

We use data from experiments on finitely repeated dilemma games with fixed matching to investigate the effect of different types of information on cooperation. The data come from 71 studies using the voluntary contributions paradigm, covering 122 data points, and from 18 studies on decision-making in oligopoly, covering another 50 data points. We find similar effects in the two sets of experimental games. We find that transparency about what everyone in a group earns reduces contributions to the public good, as well as the degree of collusion in oligopoly markets. In contrast, transparency about choices tends to lead to an increase in contributions and collusion, although the size of this effect varies somewhat between the two settings. Our results are potentially useful for policy making, because they provide guidance on the type of information to target in order to stimulate or limit cooperation.