scholarly journals On the evolutionary origins of equity

2016 ◽  
Author(s):  
Stéphane Debove ◽  
Nicolas Baumard ◽  
Jean-Baptiste André

AbstractEquity, defined as reward according to contribution, is considered a central aspect of human fairness in both philosophical debates and scientific research. Despite large amounts of research on the evolutionary origins of fairness, the evolutionary rationale behind equity is still unknown. Here, we investigate how equity can be understood in the context of the cooperative environment in which humans evolved. We model a population of individuals who cooperate to produce and divide a resource, and choose their cooperative partners based on how they are willing to divide the resource. Agent-based simulations, an analytical model, and extended simulations using neural networks provide converging evidence that equity is the best evolutionary strategy in such an environment: individuals maximize their fitness by dividing benefits in proportion to their own and their partners’ relative contribution. The need to be chosen as a cooperative partner thus creates a selection pressure strong enough to explain the evolution of preferences for equity. We discuss the limitations of our model, the discrepancies between its predictions and empirical data, and how interindividual and intercultural variability fit within this framework.

Author(s):  
Herbert Dawid ◽  
Karl Doerner ◽  
Richard F. Hartl ◽  
Marc Reimann ◽  
Georg Dorffner ◽  
...  

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
William P. J. Smith ◽  
Maj Brodmann ◽  
Daniel Unterweger ◽  
Yohan Davit ◽  
Laurie E. Comstock ◽  
...  

Abstract Tit-for-tat is a familiar principle from animal behavior: individuals respond in kind to being helped or harmed by others. Remarkably some bacteria appear to display tit-for-tat behavior, but how this evolved is not understood. Here we combine evolutionary game theory with agent-based modelling of bacterial tit-for-tat, whereby cells stab rivals with poisoned needles (the type VI secretion system) after being stabbed themselves. Our modelling shows tit-for-tat retaliation is a surprisingly poor evolutionary strategy, because tit-for-tat cells lack the first-strike advantage of preemptive attackers. However, if cells retaliate strongly and fire back multiple times, we find that reciprocation is highly effective. We test our predictions by competing Pseudomonas aeruginosa (a tit-for-tat species) with Vibrio cholerae (random-firing), revealing that P. aeruginosa does indeed fire multiple times per incoming attack. Our work suggests bacterial competition has led to a particular form of reciprocation, where the principle is that of strong retaliation, or ‘tits-for-tat’.


2019 ◽  
Vol 3 (4) ◽  
pp. 51 ◽  
Author(s):  
Georg Jäger

Agent-based modelling is a successful technique in many different fields of science. As a bottom-up method, it is able to simulate complex behaviour based on simple rules and show results at both micro and macro scales. However, developing agent-based models is not always straightforward. The most difficult step is defining the rules for the agent behaviour, since one often has to rely on many simplifications and assumptions in order to describe the complicated decision making processes. In this paper, we investigate the idea of building a framework for agent-based modelling that relies on an artificial neural network to depict the decision process of the agents. As a proof of principle, we use this framework to reproduce Schelling’s segregation model. We show that it is possible to use the presented framework to derive an agent-based model without the need of manually defining rules for agent behaviour. Beyond reproducing Schelling’s model, we show expansions that are possible due to the framework, such as training the agents in a different environment, which leads to different agent behaviour.


2010 ◽  
Vol 2 (4) ◽  
pp. 1-15 ◽  
Author(s):  
E. Grace Mary Kanaga ◽  
M. L. Valarmathi ◽  
Juliet A Murali

This paper describes an agent based approach to patient scheduling using experience based learning and an integer programming model. The evaluation on different learning techniques shows that the experience based learning (EBL) provides a better solution. The time required to process a particular job is reduced as the experience processed by it increases. The processing time can be calculated with the help of EBL. The main objective of this patient scheduling system is to reduce the waiting time of patient in hospitals and to complete their treatment in minimum required time. The proposed framework is implemented in JADE. In this approach the patients are represented as patient agent (PA) and resources as resource agent (RA). This mathematical model provides an optimal solution. The comparisons of the proposed framework with other scheduling rules shows that an agent based approach to patient scheduling using EBL gives better results.


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