Expert Game experiment predicts emergence of trust in professional communication networks

Abstract

Strong social capital is increasingly recognized as an organizational advantage. Better knowledge sharing and reduced transaction costs increase work efficiency. To mimic the formation of the associated communication network, we propose the Expert Game, where each individual must find a specific expert and receive her help. Participants act in an impersonal environment and under time constraints that provide short-term incentives for noncooperative behavior. Despite these constraints, we observe cooperation between individuals and the self-organization of a sustained trust network, which facilitates efficient communication channels with increased information flow. We build a behavioral model that explains the experimental dynamics. Analysis of the model reveals an exploitation protection mechanism and measurable social capital, which quantitatively describe the economic utility of trust.social capital | cooperation evolution | communication network | experimental economics | game theory

Resource sharing in technologically defined social networks

“…As for individual behavior, we find that subjects form largely reciprocal relations, ones where they adjust their actions in an attempt to exchange equal amounts. This finding is in line with previous studies revealing inherent reciprocity in social exchanges, even when this might result in utility loss 29,33–36 . Indeed, this is also the case here, as oftentimes non-reciprocal or asymmetric relationships might have resulted in larger resource accumulations for some players 32 .…”

“…Note that each individual has only 30 units, and hence fully reciprocated interactions are not always possible. Despite this limitation, however, our findings are consistent with a reciprocal strategy 29,33–36 instead of an exploitative strategy 37 , where the latter would have led subjects even to reduce the allocations towards the neighbors who already respond with high sharing (hence needed no further incentive to collaborate). While direct reciprocity increases with increasing number of neighbors per subject (i.e., network degree) up to a degree of 3 (i.e., when subjects have three neighbors), the impact of network degree appears to diminish when subjects have more than three neighbors in this game setting (see Supplementary Figure 4).…”

Nudging cooperation among agents in an experimental social network

“…The presence of time constraints (Fehl et al 2011 ; Rand et al 2011 ; Wang et al 2012 ; Haerter et al 2012 ; Bednarik et al 2014 ; Bendtsen et al 2016 ) together with the limited capacity of humans to focus on only a finite number of others (Fehl et al 2011 ; Hill and Dunbar 2003 ; Miritello et al 2013 ) are trademarks of human communication. Such features bring about a more complex game and make it hard to sustain an informal (non-binding) network of cooperation.…”

Oscillatory evolution of collective behavior in evolutionary games played with reinforcement learning

“…In the ecosystem and human society, the phenotypic traits of different species and their behavior characters are very complex and diverse [1,2,3,4,5,6,7,8], which remain a puzzle till now. In 1973, the pioneering framework of evolutionary game (EG) theory was proposed to investigate the frequency of the competing populations in the ecosystem by incorporating the classic game theory and the concept of evolution [9].…”