Social diversity reduces the complexity and cost of fostering fairness

Cimpeanu, Theodor; Stefano, Alessandro Di; Perret, Cédric; Han, The Anh

doi:10.1016/j.chaos.2022.113051

Cited by 16 publications

(13 citation statements)

References 52 publications

(94 reference statements)

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“…In the stochastic case, instead of copying the highest scored neighbour, at the end of each generation an agent A with score chooses to copy the strategy of a randomly selected neighbour agent B with fitness , using a pairwise comparison rule, with an imitation probability given by the Fermi–Dirac function [ 57 ]: where K denotes the amplitude of noise in the imitation process [ 56 ]. We set in our simulations, a value usually adopted in previous works [ 11 , 41 , 56 ]. Our analysis will be based on this standard evolutionary process in order to focus on understanding the cost efficiency of different interference mechanisms.…”

Section: Model and Methodsmentioning

confidence: 99%

“…However, in many scenarios, such behaviours are advocated and promoted by an external party, which is not part of the system, calling for a new set of heuristics capable of engineering a desired collective behaviour in a self-organised complex system [ 38 ]. Among these heuristics, several have been identified as capable of promoting desired behaviours at a minimal cost [ 10 , 11 , 13 , 18 , 19 , 22 , 26 , 59 ]. However, these studies neglect the diversified nature of contexts and social structures which define real-world populations.…”

Section: Introductionmentioning

confidence: 99%

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

2023

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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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Section: Model and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2023

Self Cite

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show abstract

“…Evolutionary Game Theory has been used to study pressing issues in AI Governance, and in Climate Change (Encarnação et al, 2016;Han et al, 2020;LaCroix & Mohseni, 2022;Santos et al, 2016). The field has also devoted much attention to the study of the efficiency of different incentives for resolving social dilemmas (Cimpeanu et al, 2023;Han, 2022;Sasaki et al, 2012;Sigmund et al, 2010;Sun et al, 2021). These methods have been used in the past to study games with multiple populations, as we do here (Encarnação et al, 2016;Rand et al, 2013;Santos et al, 2016;Zisis et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

“…Cimpeanu et al (2022) have already studied the competitive dynamics of AI research on heterogeneous networks. Elsewhere, Cimpeanu et al (2023) have also studied how to target incentives to foster fairness on heterogeneous networks. However, to better represent the many markets that AI companies find themselves in, as well as to identify weak links in terms of regulations, we may need to turn to a multilayer network representation (Boccaletti et al, 2014;Walsh, 2019).…”

Section: Large Ai Companies Operate In Multiple Marketsmentioning

confidence: 99%

Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety

Bova¹,

Stefano²,

Han³

2023

Preprint

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In the context of rapid discoveries by leaders in AI, governments must consider how to design regulation that matches the increasing pace of new AI capabilities. Regulatory Markets for AI is a proposal designed with adaptability in mind. It involves governments setting outcomebased targets for AI companies to achieve, which they can show by purchasing services from a market of private regulators. We use an evolutionary game theory model to explore the role governments can play in building a Regulatory Market for AI systems that deters reckless behaviour. We warn that it is alarmingly easy to stumble on incentives which would prevent Regulatory Markets from achieving this goal. These "Bounty Incentives" only reward private regulators for catching unsafe behaviour. We argue that AI companies will likely learn to tailor their behaviour to how much effort regulators invest, discouraging regulators from innovating. Instead, we recommend that governments always reward regulators, except when they find that those regulators failed to detect unsafe behaviour that they should have. These "Vigilant Incentives" could encourage private regulators to find innovative ways to evaluate cutting-edge AI systems.

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“…However, in many scenarios, such behaviours are advocated and promoted by an external party, which is not part of the system, calling for a new set of heuristics capable of engineering a desired collective behaviour in a self-organised complex system (Penn et al, 2010). Among these heuristics, several have been identified as capable of promoting desired behaviours at a minimal cost (Chen et al, 2015, Cimpeanu et al, 2023, Duong et al, 2022, Duong and Han, 2021, Wang et al, 2019. However, these studies neglect the diversified nature of contexts and social structures which define real-world populations.…”

Section: Introductionmentioning

confidence: 99%

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

Cimpeanu¹,

Santos²,

Han³

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Social diversity reduces the complexity and cost of fostering fairness

Cited by 16 publications

References 52 publications

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

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

Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety

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

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