Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups - PubMed (original) (raw)

Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups

Hirokazu Shirado et al. iScience. 2020.

Abstract

Cooperation in human groups is challenging, and various mechanisms are required to sustain it, although it nevertheless usually decays over time. Here, we perform theoretically informed experiments involving networks of humans (1,024 subjects in 64 networks) playing a public-goods game to which we sometimes added autonomous agents (bots) programmed to use only local knowledge. We show that cooperation can not only be stabilized, but even promoted, when the bots intervene in the partner selections made by the humans, re-shaping social connections locally within a larger group. Cooperation rates increased from 60.4% at baseline to 79.4% at the end. This network-intervention strategy outperformed other strategies, such as adding bots playing tit-for-tat. We also confirm that even a single bot can foster cooperation in human groups by using a mixed strategy designed to support the development of cooperative clusters. Simple artificial intelligence can increase the cooperation of groups.

Keywords: Behavioral Neuroscience; Cognitive Neuroscience; Collaborative Computing; Human-Computer Interaction.

Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

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Conflict of interest statement

Declaration of Interests The authors declare no competing interests.

Figures

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Graphical abstract

Figure 1

Figure 1

The Fraction of Cooperative Human Players per Round Light gray lines show results for each session, black lines show average across all experimental sessions for each treatment (N session = 8 per treatment). Initial rates of average cooperation varied by chance across treatments (the dashed lines). See the results of two other control conditions (“always cooperate” and “tit-for-tat”) in Figure S1. Across all 48 groups, the average initial rate of cooperation was 68.2% ± 12.8%.

Figure 2

Figure 2

Average Change in Rates of Cooperation by Round Estimates based on GLMM, using a logistic regression model of individual cooperation choice with random effects for session and individuals (see Transparent Methods). The error bars are 95% confidence interval (CI).

Figure 3

Figure 3

Cooperation Pattern with Neighborhood Change (A) Cell color shows cooperation probabilities estimated by GLMM shown in Table S2. (B) The impact of possible changes in a subject's surroundings is shown schematically. From a particular point in the parameter space (as shown in A), a person could move sideways or along the diagonal in the probabilistic space of human cooperation. Detaching or attaching to a defector changes the number of neighbors but does not change the number of cooperative neighbors; this direction obliquely crosses the contour of the cooperation probability distribution. On the other hand, detaching or attaching to a cooperator changes both the number of neighbors and the number of cooperative neighbors; such a change runs almost parallel to the contour of the cooperation probability distribution.

Figure 4

Figure 4

Cooperation and Network Dynamics with a Single Bot Deploying a Mixed Strategy (A) Control diagram for how a single bot intervenes in a network of human subjects. (B) Experiment results regarding average cooperation fraction with 95% CI (N session = 8 for each treatment). The orange line indicates the result of sessions with the single network-engineering bot. The dark gray line indicates the result of sessions without bots, which is identical to Figure 1A. (C) Experiment results regarding average rate of the bot's intervention strategy actually applied to human players, over the rounds. (D) Network snapshots of an example session having a single bot and a session without bots.

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