Model of human collective decision-making in complex environments

A continuous-time Markov process is proposed to analyze how a group of humans solves a complex task, consisting in the search of the optimal set of decisions on a fitness landscape. Individuals change their opinions driven by two different forces: (i) the self-interest, which pushes them to increase their own fitness values, and (ii) the social interactions, which push individuals to reduce the diversity of their opinions in order to reach consensus. Results show that the performance of the group is strongly affected by the strength of social interactions and by the level of knowledge of the individuals. Increasing the strength of social interactions improves the performance of the team. However, too strong social interactions slow down the search of the optimal solution and worsen the performance of the group. In particular, we find that the threshold value of the social interaction strength, which leads to the emergence of a superior intelligence of the group, is just the critical threshold at which the consensus among the members sets in. We also prove that a moderate level of knowledge is already enough to guarantee high performance of the group in making decisions.Comment: 12 pages, 8 figues in European Physical Journal B, 201

New performance indicators for industrial symbiosis: an ecosystem approach

This paper proposes new performance indicators for industrial symbiosis networks (ISNs) based on the ecosystem approach. ISNs are framed as ecosystems where the firms correspond to the organisms and perform specific functions, i.e., recovering wastes and saving inputs. Two kinds of indicators are designed: 1) indicators assessing the performance of each waste exchange; 2) indicators assessing how each firm is contributing to these exchanges. The designed indicators can be useful in backing up decision-making tools for ISNs

Efficacy of landfill tax and subsidy policies for the emergence of industrial symbiosis networks: An agent-based simulation study

Despite the theoretical value of industrial symbiosis (IS), this approach appears to be underdeveloped in terms of practical applications. Different attempts to stimulate IS in practice are noticed, one of them consisting in the application of adequate policy measures. This paper explores the efficacy of two specific policies (landfill tax and economic subsidy for IS exchanges) in supporting the emergence of self-organized industrial symbiosis networks (ISNs). We frame the ISNs as complex adaptive systems and we design an agent-based model to simulate their emergence. We use a real case study and, by means of the simulation model, we assess how the two policy measures are able to enhance the formation of spontaneous IS relationships, thereby forcing the emergence of the ISN. Results show that both policy measures have a positive effect in all scenarios considered, but the extent is strictly dependent on the environmental conditions in which IS relationships occur. The economic implications for the government are finally discussed

Advances on the Resilience of Complex Networks

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Modeling Post-disruption Equilibrium for Circular Supply Chains and New Measures for Resilience

Among concerns on sustainability and environmental protection, there is a growing interest in the adoption of circular supply chains (CSCs). This work introduces a firstcut approach to capturing the resilience of supply chains and their components related to material handling. We develop a methodology for computing the new equilibrium states after disruptions and use this to measure the resilience of circular supply chains. Numerical simulations illustrating our concept are performed on a synthetic toy-example. In addition, we introduce a secondary measure of resilience that encapsulates the transient stages of a supply chain after disruption. This measure is able to highlight the potential strength of CSCs over linear supply chains (LSCs) resulting from the circulation of reused materials. Despite the increasing hype in CSCs, caution is needed when analyzing its benefits over LSC. A CSC must be designed in way such that the positive effects of the CSC outweigh the negative effects

Mimicking the collective intelligence of human groups as an optimization tool for complex problems

This article presents a novel optimization algorithm belonging to the class of swarm intelligence optimization methods