Policy-Based Reinforcement Learning for Assortative Matching in Human Behavior Modeling

Human behavior is the potential and expressive capacity (mental, physical, and social) of human individuals or groups to respond to internal and external stimuli. We explore assortative matching as a typical human behavior in virtual networked communities. We propose a modeling approach based on MAS(Multi-Agent System) and policy-based reinforcement learning to simulate human behavior through various environmental parameter settings and agent action strategies. In our experiment, reinforcement learning serves specific agents who learn from the environment status and competitor behaviors, then optimize strategy to achieve better results. This work simulates both the individual and group level, showing some possible paths for forming relative competitive advantages. This modeling approach can help further analyze the evolutionary dynamics of human behavior, communities, and organizations on various socioeconomic topics.Comment: 2 pages, 800 words, Extended abstract for DHM of HCI International 202

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud \u

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways

A new governance approach for multi-firm projects: lessons from Olkiluoto 3 and Flamanville 3 nuclear power plant projects

We analyze governance in two contemporary nuclear power plant projects: Olkiluoto 3 (Finland) and Flamanville 3 (France). We suggest that in the governance of large multi-firm projects, any of the prevalent governance approaches that rely on market, hierarchy, or hybrid forms, is not adequate as such. This paper opens up avenues towards a novel theory of governance in large projects by adopting a project network view with multiple networked firms within a single project, and by simultaneously going beyond organizational forms that cut across the traditional firm–market dichotomy. Our analysis suggests four changes in the prevailing perspective towards the governance of large projects. First, there should be a shift from viewing multi-firm projects as hierarchical contract organizations to viewing them as supply networks characterized by a complex and networked organizational structure. Second, there should be a shift in the emphasis of the predominant modes of governance, market and hierarchy towards novel governance approaches that emphasize network-level mechanisms such as self-regulation within the project. Third, there should be a shift from viewing projects as temporary endeavors to viewing projects as short-term events or episodes embedded in the long-term sphere of shared history and expected future activities among the involved actors. Fourth, there should be a shift from the prevailing narrow view of a hierarchical project management system towards an open system view of managing in complex and challenging institutional environments

Modeling Life as Cognitive Info-Computation

This article presents a naturalist approach to cognition understood as a network of info-computational, autopoietic processes in living systems. It provides a conceptual framework for the unified view of cognition as evolved from the simplest to the most complex organisms, based on new empirical and theoretical results. It addresses three fundamental questions: what cognition is, how cognition works and what cognition does at different levels of complexity of living organisms. By explicating the info-computational character of cognition, its evolution, agent-dependency and generative mechanisms we can better understand its life-sustaining and life-propagating role. The info-computational approach contributes to rethinking cognition as a process of natural computation in living beings that can be applied for cognitive computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201

Social learning strategies modify the effect of network structure on group performance

The structure of communication networks is an important determinant of the capacity of teams, organizations and societies to solve policy, business and science problems. Yet, previous studies reached contradictory results about the relationship between network structure and performance, finding support for the superiority of both well-connected efficient and poorly connected inefficient network structures. Here we argue that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members. We show that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts. However, inefficient networks are superior when individuals follow the best member by copying the group member with the highest payoff. In addition, groups relying on conformity based on a small sample of others excel at complex tasks, while groups following the best member achieve greatest performance for simple tasks. Our findings reconcile contradictory results in the literature and have broad implications for the study of social learning across disciplines

Understanding Behavior of System of Systems Through Computational Intelligence Techniques

The world is facing an increasing level of systems integration leading towards systems of systems (SoS) that adapt to changing environmental conditions. The number of connections between components, the diversity of the components and the way the components are organized can lead to different emergent system behavior. Therefore, the need to focus on overall system behavior is becoming an unavoidable issue. The problem is to develop methodologies appropriate for better understanding behavior of system of systems before the design and implementation phase. This paper focuses on computational intelligence techniques used for analysis of complex adaptive systems with the aim of identifying areas that need methodology customization for SoS analysis