Sustainable institutionalized punishment requires elimination of second-order free-riders

Although empirical and theoretical studies affirm that punishment can elevate collaborative efforts, its emergence and stability remain elusive. By peer-punishment the sanctioning is something an individual elects to do depending on the strategies in its neighborhood. The consequences of unsustainable efforts are therefore local. By pool-punishment, on the other hand, where resources for sanctioning are committed in advance and at large, the notion of sustainability has greater significance. In a population with free-riders, punishers must be strong in numbers to keep the "punishment pool" from emptying. Failure to do so renders the concept of institutionalized sanctioning futile. We show that pool-punishment in structured populations is sustainable, but only if second-order free-riders are sanctioned as well, and to a such degree that they cannot prevail. A discontinuous phase transition leads to an outbreak of sustainability when punishers subvert second-order free-riders in the competition against defectors.Comment: 7 two-column pages, 3 figures; accepted for publication in Scientific Report

Generosity motivated by acceptance - evolutionary analysis of an anticipation game

We here present both experimental and theoretical results for an Anticipation Game, a two-stage game wherein the standard Dictator Game is played after a matching phase wherein receivers use the past actions of dictators to decide whether to interact with them. The experimental results for three different treatments show that partner choice induces dictators to adjust their donations towards the expectations of the receivers, giving significantly more than expected in the standard Dictator Game. Adding noise to the dictators' reputation lowers the donations, underlining that their actions are determined by the knowledge provided to receivers. Secondly, we show that the recently proposed stochastic evolutionary model where payoff only weakly drives evolution and individuals can make mistakes requires some adaptations to explain the experimental results. We observe that the model fails in reproducing the heterogeneous strategy distributions. We show here that by explicitly modelling the dictators' probability of acceptance by receivers and introducing a parameter that reflects the dictators' capacity to anticipate future gains produces a closer fit to the aforementioned strategy distributions. This new parameter has the important advantage that it explains where the dictators' generosity comes from, revealing that anticipating future acceptance is the key to success.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Governing the Global Commons with Local Institutions

Most problems faced by modern human society have two characteristics in common - they are tragedy-of-the-commons type of problems, and they are global problems. Tragedy-of-the-commons type of problems are those where a commonly shared resource is overexploited by free riders at the expense of everyone sharing the resource. The exploitation of global resources such as clean air and water, political stability and peace, etc. underlies many of the most pressing human problems. Punishment of free riding behavior is one of the most frequently used strategies to combat the problem, but the spatial reach of sanctioning institutions is often more limited than the spatial effects of overexploitation. Here, we analyze a general game theoretical model to assess under what circumstances sanctioning institutions with limited reach can maintain the larger commons. We find that the effect of the spatial reach has a strong effect on whether and how the commons can be maintained, and that the transitions between those outcomes are characterized by phase transitions. The latter indicates that a small change in the reach of sanctioning systems can profoundly change the way the global commons can be managed

Social Closure and the Evolution of Cooperation via Indirect Reciprocity

Direct and indirect reciprocity are good candidates to explain the fundamental problem of evolution of cooperation. We explore the conditions under which different types of reciprocity gain dominance and their performances in sustaining cooperation in the PD played on simple networks. We confirm that direct reciprocity gains dominance over indirect reciprocity strategies also in larger populations, as long as it has no memory constraints. In the absence of direct reciprocity, or when its memory is flawed, different forms of indirect reciprocity strategies are able to dominate and to support cooperation. We show that indirect reciprocity relying on social capital inherent in closed triads is the best competitor among them, outperforming indirect reciprocity that uses information from any source. Results hold in a wide range of conditions with different evolutionary update rules, extent of evolutionary pressure, initial conditions, population size, and density

Emergence of responsible sanctions without second order free riders, antisocial punishment or spite

While empirical evidence highlights the importance of punishment for cooperation in collective action, it remains disputed how responsible sanctions targeted predominantly at uncooperative subjects can evolve. Punishment is costly; in order to spread it typically requires local interactions, voluntary participation, or rewards. Moreover, theory and experiments indicate that some subjects abuse sanctioning opportunities by engaging in antisocial punishment (which harms cooperators), spiteful acts (harming everyone) or revenge (as a response to being punished). These arguments have led to the conclusion that punishment is maladaptive. Here, we use evolutionary game theory to show that this conclusion is premature: If interactions are non-anonymous, cooperation and punishment evolve even if initially rare, and sanctions are directed towards non-cooperators only. Thus, our willingness to punish free riders is ultimately a selfish decision rather than an altruistic act; punishment serves as a warning, showing that one is not willing to accept unfair treatments

Crowd computing as a cooperation problem: an evolutionary approach

Cooperation is one of the socio-economic issues that has received more attention from the physics community. The problem has been mostly considered by studying games such as the Prisoner's Dilemma or the Public Goods Game. Here, we take a step forward by studying cooperation in the context of crowd computing. We introduce a model loosely based on Principal-agent theory in which people (workers) contribute to the solution of a distributed problem by computing answers and reporting to the problem proposer (master). To go beyond classical approaches involving the concept of Nash equilibrium, we work on an evolutionary framework in which both the master and the workers update their behavior through reinforcement learning. Using a Markov chain approach, we show theoretically that under certain----not very restrictive-conditions, the master can ensure the reliability of the answer resulting of the process. Then, we study the model by numerical simulations, finding that convergence, meaning that the system reaches a point in which it always produces reliable answers, may in general be much faster than the upper bounds given by the theoretical calculation. We also discuss the effects of the master's level of tolerance to defectors, about which the theory does not provide information. The discussion shows that the system works even with very large tolerances. We conclude with a discussion of our results and possible directions to carry this research further.This work is supported by the Cyprus Research Promotion Foundation grant TE/HPO/0609(BE)/05, the National Science Foundation (CCF-0937829, CCF-1114930), Comunidad de Madrid grant S2009TIC-1692 and MODELICO-CM, Spanish MOSAICO, PRODIEVO and RESINEE grants and MICINN grant TEC2011-29688-C02-01, and National Natural Science Foundation of China grant 61020106002.Publicad

A rabbit’s tail: conspicuous rump patch causes predator confusion

Prey animals display adaptive coloration which influences the predator-prey dynamic. Tail-flagging occurs when a prey animal makes itself highly conspicuous by raising its white tail whilst fleeing from a predator. This behaviour (seen in ungulates and cottontail rabbits) seems maladaptive because it appears to increase (not reduce) predator attention and pursuit. In terms of evolutionary function, there is no consensus on why tail-flagging occurs. Here we show that the function is likely based on predator confusion, due to perceptual distraction from the ‘blinking tail’ stimulus emerging from the rhythmic motion of the running prey. We tested 24 human ‘predators’ in a computer game where they were asked to repeatedly ‘follow’ a fleeing rabbit stimulus (represented by a 28mm circle moving across a scrolling grass-coloured field) by clicking one of three buttons – left, right, or unsure –after the rabbit makes a sudden lateral escape to the left or right (at unpredictable time intervals and direction). There were two conditions: (1) no-tail, where the rabbit-circle is the same colour as the scrolling field, and (2) with-tail, where the rabbit-circle is superimposed by a same-sized blinking white circle. We found that, for the blinking tail condition, correct replies are significantly lower and wrong/uncertain replies were significantly higher. Furthermore, we partitioned responses into ‘fast’ or ‘slow’ (above or below 500ms) and found that correct replies for the blinking tail condition were significantly higher only in slow responses. Our results demonstrate the adaptive advantage of tail-flagging because it impedes the predator’s lateral movement during pursuit