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Intro -- Preface -- Contents -- Contributors -- Sociality, Norms and Institutions -- Efficient Redistribution of Scarce Resources Favours Hierarchies -- 1 Introduction -- 1.1 Resource Models as Means of Redistribution -- 1.2 Previous Research -- 2 Methods -- 2.1 Model Design -- 2.2 Simulations, Model Parameters, and Variables -- 3 Results -- 4 Discussion -- 4.1 Present Findings -- 4.2 Model Validation: Intricacies in the Relations Between Resources and Redistribution Rules -- 5 Conclusion -- References -- Evaluation of COVID-19 Infection Prevention Measures Compatible with Local Economy -- 1 Introduction -- 1.1 COVID-19 Agent-Based Simulations -- 1.2 Summary of Related Studies and Positioning of This Study -- 2 COVID-19 Infection Model for Tourism Locations -- 2.1 Behaviour of Citizen -- 2.2 Progress of Infection and Symptoms -- 3 Estimating Effects of Infection Prevention Measures -- 4 Experiment Results -- 5 Discussion -- 6 Conclusion -- References -- DIPP: Diffusion of Privacy Preferences in Online Social Networks -- 1 Introduction -- 2 Modelling Privacy Preference Diffusion -- 2.1 SIR Model for Privacy Preference Diffusion -- 2.2 Trust -- 2.3 Co-ownership -- 3 Realization of the DIPP Model -- 4 Evaluation -- 4.1 Measurements -- 4.2 Experiments -- 5 Conclusion -- References -- Explaining and Resolving Norm-Behavior Inconsistencies-A Theoretical Agent-Based Model -- 1 Theoretical Background -- 1.1 Norm-Behavior Inconsistencies -- 1.2 A Norm-Based Theory of Decision-Making -- 2 Method -- 2.1 Agents' Decision-Making -- 2.2 Norm Internalization and Its Effects on Decision-Making -- 3 Results -- 3.1 Explaining Norm-Behavior Inconsistencies -- 3.2 Resolving Norm-Behavior Inconsistencies -- 4 Discussion -- 4.1 Explaining and Resolving Norm-Behavior Inconsistencies -- 4.2 Limitations and Future Research -- 4.3 Conclusion -- References.

Efficient Redistribution of Scarce Resources Favours Hierarchies -- Evaluation of COVID-19 Infection Prevention Measures Compatible with Local Economy -- DIPP: Diffusion of Privacy Preferences in Online Social Networks -- Explaining and Resolving Norm-behavior Inconsistencies – A Theoretical Agent-based Model -- Towards More Realism in Pedestrian Behaviour Models First Steps and Considerations in Formalising Social Identity -- Developing a Stakeholder-centric Simulation Tool to Support Integrated Mobility Planning -- Support Local Empowerment Using Various Modeling Approaches and Model Purposes: a Practical and Theoretical Point of View -- Exploring Coevolutionary Dynamics Between Infinitely Diverse Heterogenous Adaptive Automated Trading Agents -- Pay-for-Performance and Emerging Search Behavior: When Exploration Serves to Reduce Alterations -- Effects of Limited and Heterogeneous Memory in Hidden-action Situations -- Autonomous Group Formation of Heterogeneous Agents in Complex Task Environments -- Exploring Regional Agglomeration Dynamics in Face of Climate-driven Hazards: Insights from an Agent-based Computational Economic Model -- Dynamics of Wealth Inequality in Simple Artificial Societies -- Cruising Drivers' Response to Changes in Parking Prices in a Serious Game -- Quantum Leaper : A Methodology Journey From a Model in NetLogo to a Game in Unity -- How Perceived Complexity Impacts on Comfort Zones in Social Decision Contexts - Combining Gamification and Simulation for Assessment -- A Hybrid Agent-based Model to Simulate and Re-think Post-COVID-19 Use Processes in Educational Facilities -- Generation Gaps: An Agent-Based Model of Opinion Shifts among Cohorts -- Comparison of Viral Information Spreading Strategies in Social Media -- An Evidence-Driven Model of Voting and Party Competition -- Shrinking Housing's Size Using Agent-based Modelling to Explore Measures for a Reduction of Floor Area per Capita -- The Problem with Bullying: Lessons Learned from Modelling Marginalization with Diverse Stakeholders -- On the Impact of Misvaluation on Bilateral Trading -- Consumer Participation in Demand Response Programs: Development of a Consumat-based Toy Model -- Using MBTI Agents to Simulate Human Behavior in a Work Context.

AbstractMost of the current complex networks that are of interest to practitioners possess a certain community structure that plays an important role in understanding the properties of these networks. For instance, a closely connected social communities exhibit faster rate of transmission of information in comparison to loosely connected communities. Moreover, many machine learning algorithms and tools that are developed for complex networks try to take advantage of the existence of communities to improve their performance or speed. As a result, there are many competing algorithms for detecting communities in large networks. Unfortunately, these algorithms are often quite sensitive and so they cannot be fine-tuned for a given, but a constantly changing, real-world network at hand. It is therefore important to test these algorithms for various scenarios that can only be done using synthetic graphs that have built-in community structure, power law degree distribution, and other typical properties observed in complex networks. The standard and extensively used method for generating artificial networks is the LFR graph generator. Unfortunately, this model has some scalability limitations and it is challenging to analyze it theoretically. Finally, the mixing parameter μ, the main parameter of the model guiding the strength of the communities, has a non-obvious interpretation and so can lead to unnaturally defined networks. In this paper, we provide an alternative random graph model with community structure and power law distribution for both degrees and community sizes, the Artificial Benchmark for Community Detection (ABCD graph). The model generates graphs with similar properties as the LFR one, and its main parameter ξ can be tuned to mimic its counterpart in the LFR model, the mixing parameter μ. We show that the new model solves the three issues identified above and more. In particular, we test the speed of our algorithm and do a number of experiments comparing basic properties of both ABCD and LFR. The conclusion is that these models produce graphs with comparable properties but ABCD is fast, simple, and can be easily tuned to allow the user to make a smooth transition between the two extremes: pure (independent) communities and random graph with no community structure.

PurposeThe purpose of the paper is to advance the understanding of the links between the presence of independent directors (IDs) on boards and the company value in the specific context of concentrated ownership. The authors apply the framework of agency theory to identify the monitoring effect of IDs in two legal systems – common law and civil law.Design/methodology/approachThe authors test formulated hypotheses using a unique sample of 50 Mauritian and Polish companies listed during the years 2007 to 2015, amounting to a total of 394 observations adopting the fixed effect panel model.FindingsThe results of the panel model show a negative relationship between independent directors on boards and company value. Specifically, the effect remains negative for companies operating in the civil law system, whereas the stronger protection offered by common law offsets the effect of concentrated ownership, resulting in a non-correlation between independent directors on board and firm value.Originality/valueThis study expands the understanding of the value added by independent directors, addressing their monitoring role in the unfavorable context of concentrated ownership. It also reveals that different legal frameworks of civil law and common law may impact the monitoring performed by independent directors.

AbstractGraph embedding is a transformation of nodes of a network into a set of vectors. A good embedding should capture the underlying graph topology and structure, node-to-node relationship, and other relevant information about the graph, its subgraphs, and nodes themselves. If these objectives are achieved, an embedding is a meaningful, understandable, and often compressed representation of a network. Unfortunately, selecting the best embedding is a challenging task and very often requires domain experts. In this paper, we extend the framework for evaluating graph embeddings that was recently introduced in [15]. Now, the framework assigns two scores, local and global, to each embedding that measure the quality of an evaluated embedding for tasks that require good representation of local and, respectively, global properties of the network. The best embedding, if needed, can be selected in an unsupervised way, or the framework can identify a few embeddings that are worth further investigation. The framework is flexible and scalable and can deal with undirected/directed and weighted/unweighted graphs.