Abstract
The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent in computer science and business circles. Here we consider a primordial form of cooperation – imitative learning – that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent – the best performing agent in its influence network. There is a trade-off between the number of agents and the imitation probability , and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group.
Highlights
Imitative learning or, more generally, social learning offers a means whereby information can be transferred between biological or artificial agents, being a crucial factor for the emergence of social intelligence or collective brains [1]
The efficiency of a search strategy is measured by the total number of agent updates necessary to find the solution of the cryptarithmetic problem (i.e., NtÃ) and in the following we will refer to this measure as the computational cost of the search
Since we expect that the typical number of trials to success tà scales with the size of the solution space (i.e., 10!), we will present the results in terms of the rescaled variable t~tÃ=10!
Summary
More generally, social learning offers a means whereby information can be transferred between biological or artificial agents, being a crucial factor for the emergence of social intelligence or collective brains [1]. The reason is probably that those heuristics and the problems they are set to solve are too complex to yield to a first-principle analysis In this contribution we address these issues by tackling a simple combinatorial problem and by endowing the agents with straightforward search strategies in which the strength of collaboration is controlled by a single parameter of the model
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