Emergent Service Provisioning and Demand Estimation through Self-Organizing Agent Communities

Abstract

A major challenge within open markets is the ability to satisfy service demand with an adequate supply of service providers, especially when such demand may be volatile due to changing requirements, or fluctuations in the availability of services. Ideally, the supply and demand of services should be balanced; however, when consumer demand change over time, and providers can independently choose which services they provide, a coordination problem known as ‘herding’ can arise, bringing instability to the market. This behavior can emerge when consumers share similar preferences for the same providers, and thus compete for the same resources. Likewise, providers which share estimates of fluctuating service demand may respond in unison, withdrawing some services to introduce others, and thus oscillate the available supply around some ideal equilibrium. One approach to avoid this unstable behavior is to limit the flow of information across the agent community, such that agents possess an incomplete and subjective view of the local service availability and demand. By drawing inspiration from information flow within biological systems, we propose a model of an adaptive service-offering mechanism, in which providers adapt their choice of services that they offer to consumers, based on perceived demand. By varying the volume of information shared by agents, we demonstrate that a co-adaptive equilibrium can be achieved, thus avoiding the herding problem. As the knowledge that agents can possess is limited, agents self-organise into community structures that support locally shared information. We demonstrate that such a model is capable of reducing instability in service demand and thus increase utility (based on successful service provision) by up to 59%, when compared to the use of globally available information.

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