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In this work, we suggest a quantum-like simulator concept to study social processes related to the solution of NP-hard problems. The simulator is based on the solaser model recently proposed by us in the framework of information cascade growth and echo chamber formation in social network communities. The simulator is connected with the random laser approach that we examine in the A and D-class (superradiant) laser limits. Novel network-enforced cooperativity parameters of decision-making agents, which may be measured as a result of the solaser simulation, are introduced and justified for social systems. The innovation diffusion in complex networks is discussed as one of the possible impacts of our proposal. [ABSTRACT FROM AUTHOR]

In this work, we suggest a quantum-like simulator concept to study social processes related to the solution of NP-hard problems. The simulator is based on the solaser model recently proposed by us in the framework of information cascade growth and echo chamber formation in social network communities. The simulator is connected with the random laser approach that we examine in the A and D-class (superradiant) laser limits. Novel network-enforced cooperativity parameters of decision-making agents, which may be measured as a result of the solaser simulation, are introduced and justified for social systems. The innovation diffusion in complex networks is discussed as one of the possible impacts of our proposal.

In many practical contexts, it is often required to classify some objects of interest into predetermined unordered quality categories. This operation—referred to as quality classification problem—has received considerable attention in many fields of research, such as Analytical Chemistry, Materials Science, Medicine, Manufacturing, Quality Engineering/Management, Decision Analysis. Assuming that multiple agents perform subjective assignments of categories to the objects of interest, a further problem is that of fusing these assignments into global classifications. To this purpose, the mode and the weighted mode are very practical measures, as long as agents are equi-important or their (different) importance is expressed in the form of a set of weights. Unfortunately, these measures are not appropriate for quality classification problems where the agents’ importance is expressed in the form of a rank ordering (hierarchy). The aim of this article is to present a new method, which addresses the latter quality classification problem in a relatively simple and practical way. The peculiarity of this method is that the different importance of agents determines a different priority in considering their assignments and not a different weight of these assignments. A detailed description of the new method is supported by a realistic example in the Analytical Chemistry field. [ABSTRACT FROM AUTHOR]

Abstract: Multi-armed bandit tasks have been extensively used to model the problem of balancing exploitation and exploration. A most challenging variant of the MABP is the non-stationary bandit problem where the agent is faced with the increased complexity of detecting changes in its environment. In this paper we examine a non-stationary, discrete-time, finite horizon bandit problem with a finite number of arms and Gaussian rewards. A family of important ad hoc methods exists that are suitable for non-stationary bandit tasks. These learning algorithms that offer intuition-based solutions to the exploitation–exploration trade-off have the advantage of not relying on strong theoretical assumptions while in the same time can be fine-tuned in order to produce near-optimal results. An entirely different approach to the non-stationary multi-armed bandit problem presents itself in the face of evolutionary algorithms. We present an evolutionary algorithm that was implemented to solve the non-stationary bandit problem along with ad hoc solution algorithms, namely action-value methods with e-greedy and softmax action selection rules, the probability matching method and finally the adaptive pursuit method. A number of simulation-based experiments was conducted and based on the numerical results that we obtained we discuss the methods’ performances. [Copyright &y& Elsevier]