Exploring the Importance of Stochasticity to Hybrid Equilibria in a Discrete Signaling Game

Communication via evolved signals is ubiquitous (both within and between species) in the natural world. However, how honest we should expect signals to be remains an open question. Hybrid equilibria are a form of equilibria predicted by discrete signaling games in which signalers are sometimes dishonest and signals do not completely reliably convey information on signaler quality. While these equilibria have been theoretically demonstrated in several signaling games, their dynamics in a stochastic simulation of evolutionary trajectories (that include representation of the inherent noise expected in evolution in the natural world) have not previously been studied. In this paper, we present an agent-based simulation of a discrete signaling game which exhibits hybrid equilibria. We find that while hybrid equilibria are evolutionarily attractive where they exist, populations exhibit vairable and often drastic oscillating behavior around the predicted equilibrium values. We discuss how these..., Included in this repository: Data from agent-based simulations. Code for simulations presented in the paper. , , # Raw data for Exploring the Importance of Stochasticity to Hybrid Equilibria in a Discrete Signaling Game [Access this dataset on Dryad](https://doi.org/10.5061/dryad.7d7wm384b) This dataset contains results of simulations of a discrete signaling game. Results for two main experiments are given. One experiment allowed agents to play the signalling game starting at a nonsignaling or honest signaling state in order to determine which equilibria they reach after 1,000,000 generations. The other experiment focuses on trajectories of these simulations, studying how differential mutation rates for senders and receivers affects oscillations in strategies through time. ## Description of the data and file structure (in Data.zip) ### Equilibria\_Honest Start and Equilibria\_No Signaling Start folders: These folder contain raw data for simulations used to make figure 3 in the manuscript and supplementary figures 1-2. Simulations initialized at an honest signaling state (Equilibria_Honest S...自然界中，通过演化信号实现的通信在物种内部及跨物种间无处不在。然而，我们应当在多大程度上预期信号具备诚实性，这仍是一个悬而未决的学术问题。混合均衡（hybrid equilibria）是离散信号博弈（discrete signaling games）所预测的一类均衡形式：在此类均衡中，信号发送者有时会表现出不诚实行为，且信号无法完全可靠地传递与发送者质量相关的信息。尽管这类均衡已在多个信号博弈中得到理论验证，但此前尚未有研究针对包含自然演化固有噪声的随机演化轨迹模拟，探究此类均衡的动态特征。 本文提出了一种可呈现混合均衡的基于智能体的模拟（agent-based simulation）模型，该模型基于离散信号博弈。研究发现，尽管混合均衡在其存在的场景下具备演化吸引力，但种群会在预测的均衡值附近呈现出可变且往往剧烈的振荡行为。本文还将探讨…… 本仓库包含以下内容：基于智能体的模拟所得的实验数据，以及本文所呈现的模拟代码。 # 本数据集为《探究随机性对离散信号博弈中混合均衡的重要性》的原始数据 [可在Dryad平台获取本数据集](https://doi.org/10.5061/dryad.7d7wm384b) 本数据集包含离散信号博弈的模拟结果，涵盖两项核心实验： 第一项实验设置为：令智能体从非信号传递或诚实信号传递的初始状态启动信号博弈，以探明其在100万代演化后所能达到的均衡状态。第二项实验则聚焦于模拟的演化轨迹，探究发送者与接收者的差异化突变率如何随时间影响策略的振荡变化。 ## 数据与文件结构说明（详见Data.zip） ### Equilibria_Honest Start与Equilibria_No Signaling Start文件夹： 此类文件夹包含用于绘制稿件中图3及补充图1至2的模拟原始数据。 模拟初始化设置为诚实信号传递状态（Equilibria_Honest S...）