Social decision-making in a large-scale MultiAgent system considering the influence of empathy

E mpathy is the ability to spontaneously or purposefully place oneself in another’s situation. Under the continuous effect of empathy, an individual’s preference for things will inevitably be affected by the local and non-local social environment. Inspired by neuropsychology, this paper constructs an extended empathy model to compensate for the shortcomings of previous models in describing the global preference (utility) coupling between individuals, and analyzes how to make efficient decisions based on this model in a large-scale multiagent system. Empathy is abstracted as a random experience process in the form of nonstationary Markov chains, and empathetic utility is defined as the expectation of preference experienced under the corresponding transition probability distribution. By structurally introducing the self-other separation mechanism and energy attenuation mechanism, the model can exhibit social attributes, including absorbency, inhibition, and anisotropy. An extended iterative candidate elimination (EICE) algorithm is designed for the decision problem defined by the proposed model. This algorithm correlates the error upper bound of the objective function with that of the empathy utility to perform the iterative estimation of the candidate strategies.

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