Does Spending More Always Ensure Higher Cooperation? An Analysis of Institutional Incentives on Heterogeneous Networks.

IF 1.8 4区 数学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Dynamic Games and Applications Pub Date : 2023-04-04 DOI:10.1007/s13235-023-00502-1
Theodor Cimpeanu, Francisco C Santos, The Anh Han
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Abstract

Humans have developed considerable machinery used at scale to create policies and to distribute incentives, yet we are forever seeking ways in which to improve upon these, our institutions. Especially when funding is limited, it is imperative to optimise spending without sacrificing positive outcomes, a challenge which has often been approached within several areas of social, life and engineering sciences. These studies often neglect the availability of information, cost restraints or the underlying complex network structures, which define real-world populations. Here, we have extended these models, including the aforementioned concerns, but also tested the robustness of their findings to stochastic social learning paradigms. Akin to real-world decisions on how best to distribute endowments, we study several incentive schemes, which consider information about the overall population, local neighbourhoods or the level of influence which a cooperative node has in the network, selectively rewarding cooperative behaviour if certain criteria are met. Following a transition towards a more realistic network setting and stochastic behavioural update rule, we found that carelessly promoting cooperators can often lead to their downfall in socially diverse settings. These emergent cyclic patterns not only damage cooperation, but also decimate the budgets of external investors. Our findings highlight the complexity of designing effective and cogent investment policies in socially diverse populations.

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增加支出总是能确保更高的合作吗?异构网络的制度激励分析。
人类已经发展出相当大的机制,用于大规模制定政策和分配激励措施,但我们永远在寻找改进这些机制的方法,我们的制度。特别是在资金有限的情况下,必须在不牺牲积极成果的情况下优化支出,这是社会、生命和工程科学的几个领域经常面临的挑战。这些研究往往忽视了信息的可用性、成本限制或潜在的复杂网络结构,这些结构定义了现实世界中的人群。在这里,我们扩展了这些模型,包括前面提到的问题,但也测试了他们的发现对随机社会学习范式的稳健性。根据现实世界中关于如何最好地分配捐赠的决策,我们研究了几种激励方案,这些方案考虑了有关总体人口、当地社区或合作节点在网络中的影响力水平的信息,如果满足某些标准,则有选择地奖励合作行为。在向更现实的网络环境和随机行为更新规则过渡之后,我们发现,在社会多样化的环境中,不小心提拔合作者往往会导致他们的失败。这些新出现的周期性模式不仅损害了合作,还大幅削减了外部投资者的预算。我们的研究结果突显了在社会多样化人群中设计有效和令人信服的投资政策的复杂性。
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来源期刊
Dynamic Games and Applications
Dynamic Games and Applications MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
3.20
自引率
13.30%
发文量
67
期刊介绍: Dynamic Games and Applications is devoted to the development of all classes of dynamic games, namely, differential games, discrete-time dynamic games, evolutionary games, repeated and stochastic games, and their applications in all fields
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