Narrow Results

Ostracism is one form of real-life punishment mechanism to penalize uncooperative individuals in many societies. This paper aims to test whether ostracism enhances cooperation in the public good experiment by allowing group members to expel others based on the majority voting rule. The study also employed the modified public good experiment to categorize the contributors. The results showed that introducing ostracism increases contribution level by about 45 percentage points on average. However, some participants punished other members, even they contributed less than others or so-called anti-social punishment, suggesting that a third-party punishment may be another solution to sustain cooperation.

This article analyzes crime development which is one of the largest threats in today's world, frequently referred to as the war on crime. The criminal commits crimes in his free time (when not in jail) according to a non-stationary Poisson process which accounts for fluctuations. Expected values and variances for crime development are determined. The deterrent effect of imprisonment follows from the amount of time in imprisonment. Each criminal maximizes expected utility defined as expected benefit (from crime) minus expected cost (imprisonment). A first-order differential equation of the criminal's utility-maximizing response to the given punishment policy is then developed. The analysis shows that if imprisonment is absent, criminal activity grows substantially. All else being equal, any equilibrium is unstable (labile), implying growth of criminal activity, unless imprisonment increases sufficiently as a function of criminal activity. This dynamic approach or perspective is quite interesting and has to our knowledge not been presented earlier. The empirical data material for crime intensity and imprisonment for Norway, England and Wales, and the US supports the model. Future crime development is shown to depend strongly on the societally chosen imprisonment policy. The model is intended as a valuable tool for policy makers who can envision arbitrarily sophisticated imprisonment functions and foresee the impact they have on crime development.

To remember or forget our acquaintances’ strategies can influence our decision-making significantly. In this paper, we explore the evolutionary prisoner’s dilemma (PD) game model with punishment and memory mechanism in the time-varying network. Our results show that a larger temptation gain $T$ or a larger the number of connected edges of activated individuals $m$ would result in the decrease of the final fraction of cooperators. However, with the increase of the maximum penalty cost, the maximum punishment intensity or the value of individual’s “memory factor”, players are more inclined to choose cooperative strategy. In addition, an effective way to promote the cooperation is to improve the social subsidy. Remarkably, only when the social subsidy is greater than the temptation gain, the density of cooperators could increase significantly. Interestingly, there is a linear relationship between the threshold of social subsidy and the temptation gain. The final results show that ones’ activity rates have no significant correlation with their strategies.

Incentive institutions that reward cooperators and punish free-riders are often used to promote cooperation in public goods games. We show that for incentives of intermediate size, a sanctioning institution that punishes the worst players can sustain full cooperation and that a rewarding institution can promote cooperation only if lower contributors also have the chance to win the reward. Furthermore, if the incentive institution can provide both reward and punishment, then it should use reward as much as possible. The group welfare is maximized when the punishment is just barely larger than the minimum required to obtain the full contribution.

I study two mechanisms based on punishment to promote cooperation in the well-mixed two-population snowdrift game (SG). The first mechanism follows a standard approach in the literature and is based on the inclusion of a third additional pure strategy in the payoff matrix of the stage-game. Differently, the second mechanism consists of letting cooperators punish defectors with a given exogenous frequency. In the latter, the pure strategy cooperation is replaced by a mixed strategy in which cooperators randomize between cooperation and punishment against defectors. While both mechanisms share the same result regarding the minimum required level of punishment in order to eliminate defectors in both populations, stability in the mechanism following the second approach is more robust in the sense that extinction of defectors is a globally asymptotically stable state for any interior initial conditions in the phase space. Thus, the second mechanism displays a topologically simpler model but the robustness of the evolutionary equilibrium is improved. Results were obtained analytically through nonlinear differential equations and also using an agent-based simulation. There was a good level of agreement between both approaches with respect to the evolutionary pattern over time and the possible steady-states.

The occasional “strike hard” campaigns against crime launched by the Chinese government provide an opportunity to isolate the separate effects of severity and certainty of punishment on the crime rate. The “strike hard” campaigns increase the severity of the punishment but keep the certainty of the punishment unchanged. We use provincial panel data from 1988 to 2015 to examine the impacts of the two strategies on the crime rate with pooled mean group models. The empirical results show that a significant decrease in crime rates is associated with greater certainty of detection, but greater severity has no significant effect. A 1% increase in the detection rate (a measurement of certainty) predicts about 2.7% lower crime rate. The results are robust even after considering the endogenous nature of punishment policies and controlling for the measurement error in the officially reported data.

Punishment and reward are usually regarded as two potential mechanisms to explain the evolution of cooperation especially among multiple participators. However, the performance of these two scenarios in spatial environment needs to be discussed. To figure out this issue, we resort to the $n$-player Iterated Snowdrift Dilemma (ISD) game and Iterated Prisoner’s Dilemma (IPD) game. More importantly, the evolution of punishment and reward in social network-structured populations has not been formally addressed. The numerical results show the equilibrium cooperation frequency can be influenced by cost-to-benefit ratio $r$, the punishment-to-benefit ratio $p∕b$ and the reward-to-benefit ratio $r∕b$. And one intriguing observation is that under the same situation, the punishment is more effective than reward to the population. Then we further probe the effectiveness of neighborhood relationship to the cooperation, which is reflected by the random rewired probability $p_r$. From the distribution of the four roles of the participator we can find that individuals can cooperate easily when they have close relationship. The results of this paper may be helpful to understand the cooperation in complex project or among industry–university–research cooperation project.

Corruption is widespread in Bangladesh. According to a report of the international watchdog Transparency International, during 2001–2005, Bangladesh was the most corrupt country in the world. How to combat corruption in Bangladesh is now an important agenda for both international donor agencies and the government. Using game theory, this paper proposes that a successful reduction in corruption to a tolerable level in the government sector depends on a reduction in both incentives to and opportunities for corruption by government employees, and this can be done by raising salaries and introducing strong punishment simultaneously. To introduce an effective and strong punishment system, independent, and strong anti-corruption bodies and strong commitment from political leaders are the essential conditions.