Abstract
Conditional cooperation declines over time if heterogeneous ideal conditional agents are involved in repeated interactions. With strict assumptions of rationality and a population consisting of ideal conditional agents who strictly follow a decision rule, cooperation is not expected. However, cooperation is commonly observed in human societies. Hence, we propose a novel evolutionary agent-based model where agents rely on social information. Each agent interacts only once either as a donor or as a receiver. In our model, the population consists of either non-ideal or ideal heterogeneous conditional agents. Their donation decisions are stochastically based on the comparison between the number of donations in the group and their conditional cooperative criterion value. Non-ideal agents occasionally cooperate even if the conditional rule of the agent is not satisfied. The stochastic decision and selection rules are controlled with decision intensity and selection intensity, respectively. The simulations show that high levels of cooperation (more than 90%) are established in the population with non-ideal agents for a particular range of parameter values. The emergence of cooperation needs non-ideal agents and a heterogeneous population. The current model differs from existing models by relying on social information and not on individual agent’s prior history of cooperation.
Highlights
Apart from psychology based explanations, there have been physics or game theory inspired models to explain conditional cooperation[5,11,12,13,14,15,16,17,18,19,20]
Our results demonstrate that stable and high levels of cooperation are achieved in a heterogeneous population with non-ideal conditional agents
The results shows that for certain range of parameter values [β: (0.1 < β < 2) and η:(0.1 < η < 2)], high donation rates are observed in the population
Summary
Conditional cooperation declines over time if heterogeneous ideal conditional agents are involved in repeated interactions. The current model with the proposed donation decision rules goes beyond the existing standard game theoretical models of conditional cooperation and is different from the evolutionary models of human cooperation[37] but in line with physics inspired models of conditional cooperation[11] The standard mechanisms such as kin selection[38], direct reciprocity[39], indirect reciprocity[40], spatial selection[41], and group selection[42] require either private or public information about the specific agents with whom they are going to interact in the future. We show that depending on the degree of non-ideal nature of the agent, a population consisting of heterogeneous conditional agents interacting with each other and adapting successful agents’ CCC value can reach higher levels of stable cooperation. The non-ideal aspect of agent is modeled by using decision intensity (β), which controls occasional mistakes in an agent’s conditional cooperative decisions and selection intensity (η), which controls occasional mistakes in copying successful agents’ behavior or CCC values. We allow agents to copy a random CCC value, which is drawn from uniform distribution with range [1, n] with probability 0.1
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
