scholarly journals Agent-based simulation for reconstructing social structure by observing collective movements with special reference to single-file movement

2020 ◽  
Author(s):  
Hiroki Koda ◽  
Zin Arai ◽  
Ikki Matsuda
Keyword(s):  
Nearest Neighbor ◽  
Social Structures ◽  
Spatial Distributions ◽  
Simple Assumption ◽  
Agent Based ◽  
Movement Data ◽  
Single File ◽  
Field Investigations ◽  
Collective Movements ◽  
Dependent Agents

AbstractUnderstanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data ‒serialized order movements generated by simple bottom-up rules of collective movements— are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations ‒a coupling of “core” or “keystone” subjects and “subordinate” or “follower” subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating “single-file movement” data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.

scholarly journals Agent-based simulation for reconstructing social structure by observing collective movements with special reference to single-file movement

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243173
Author(s):  
Hiroki Koda ◽  
Zin Arai ◽  
Ikki Matsuda
Keyword(s):  
Nearest Neighbor ◽  
Social Structures ◽  
Spatial Distributions ◽  
Simple Assumption ◽  
Agent Based ◽  
Movement Data ◽  
Single File ◽  
Field Investigations ◽  
Collective Movements ◽  
Dependent Agents

Understanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data—serialized order movements generated by simple bottom-up rules of collective movements—are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations—a coupling of “core” or “keystone” subjects and “subordinate” or “follower” subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating “single-file movement” data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.

scholarly journals Clustering knowledge and dispersing abilities enhances collective problem solving in a network

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Charles J. Gomez ◽  
David M. J. Lazer
Keyword(s):  
Problem Solving ◽  
Agent Based Modeling ◽  
Social Structures ◽  
Cognitive Diversity ◽  
Agent Based ◽  
Local Homogeneity ◽  
Local Diversity ◽  
Long Run ◽  
Social Settings ◽  
Deliberative Groups

Abstract Diversity tends to generate more and better ideas in social settings, ranging in scale from small-deliberative groups to tech-clusters and cities. Implicit in this research is that there are knowledge-generating benefits from diversity that comes from mixing different individuals, ideas, and perspectives. Here, we utilize agent-based modeling to examine the emergent outcomes resulting from the manipulation of how diversity is distributed and how knowledge is generated within communicative social structures. In the context of problem solving, we focus on cognitive diversity and its two forms: ability and knowledge. For diversity of ability, we find that local diversity (intermixing of different agents) performs best at all time scales. However, for diversity of knowledge, we find that local homogeneity performs best in the long-run, because it maintains global diversity, and thus the knowledge-generating ability of the group, for a longer period.

A Test of Randomness for Finite Spatial Distributions

1994 ◽  
Vol 78 (3) ◽  
pp. 707-714 ◽  
Author(s):  
Frank O'brien
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Null Hypothesis ◽  
Nearest Neighbor ◽  
Dimensional Space ◽  
Spatial Distributions ◽  
Two Dimensional ◽  
Model Extension ◽  
Spatially Distributed ◽  
Test Of Randomness

A statistical method is presented for determining randomness of points spatially distributed in two-dimensional space. The procedure is based on a distance-to-particle (nearest neighbor) model derived from an elementary Poisson process. In a previous derivation of the method, an extension to the model was proposed and used without adequate empirical justification. Herein the test is derived in detail and its performance evaluated with Monte Carlo simulations. Results indicate that the model extension provides adequate representations when the null hypothesis is true.

scholarly journals A framework for understanding the architecture of collective movements using pairwise analyses of animal movement data

2010 ◽  
Vol 8 (56) ◽  
pp. 322-333 ◽  
Author(s):  
Leo Polansky ◽  
George Wittemyer
Keyword(s):  
Information Exchange ◽  
Animal Movement ◽  
Interspecific Interactions ◽  
Movement Patterns ◽  
African Elephants ◽  
Collective Movement ◽  
Movement Trajectories ◽  
Movement Data ◽  
Collective Movements ◽  
Free Ranging

The study of collective or group-level movement patterns can provide insight regarding the socio-ecological interface, the evolution of self-organization and mechanisms of inter-individual information exchange. The suite of drivers influencing coordinated movement trajectories occur across scales, resulting from regular annual, seasonal and circadian stimuli and irregular intra- or interspecific interactions and environmental encounters acting on individuals. Here, we promote a conceptual framework with an associated statistical machinery to quantify the type and degree of synchrony, spanning absence to complete, in pairwise movements. The application of this framework offers a foundation for detailed understanding of collective movement patterns and causes. We emphasize the use of Fourier and wavelet approaches of measuring pairwise movement properties and illustrate them with simulations that contain different types of complexity in individual movement, correlation in movement stochasticity, and transience in movement relatedness. Application of this framework to movements of free-ranging African elephants ( Loxodonta africana ) provides unique insight on the separate roles of sociality and ecology in the fission–fusion society of these animals, quantitatively characterizing the types of bonding that occur at different levels of social relatedness in a movement context. We conclude with a discussion about expanding this framework to the context of larger (greater than three) groups towards understanding broader population and interspecific collective movement patterns and their mechanisms.

scholarly journals Understanding the Characteristics of Pedestrians when Passing Obstacles of Different Sizes: An Experimental Study

2021 ◽  
Vol 6 ◽  
Author(s):  
Abdullah N Alhawsawi ◽  
Majid Sarvi ◽  
Emad Felemban ◽  
Abbas Rajabifard ◽  
Jianyu Wang
Keyword(s):  
Experimental Study ◽  
Flow Rate ◽  
High Speed ◽  
The Body ◽  
Agent Based ◽  
Significant Difference ◽  
Speed Level ◽  
Collective Movements ◽  
The Right ◽  
Insight Into

The aim of this study is to understand the collective movements of individuals and to observe how individuals interact within a physical environment in a crowd dynamic, which has drawn the attention of many researchers. We conducted an experimental study to observe interactions in the collective motions of people and to identify characteristics of pedestrians when passing obstacles of different sizes (bar-shaped, 1.2 m, 2.4 m, 3.6 m and 4.8 m), going through one narrow exit and employing three different flow rates in walking and running conditions. According to the results of our study, there were no differences in collision-avoidance behaviour of pedestrians when walking or running. The pedestrians reacted early to the obstacles and changed the direction in which they were walking by quickly turning to the left or to the right. In terms of the speed of the pedestrians, the average velocity was significantly affected while performing these tasks, decreasing as the size of the obstacle increased; therefore, the size of obstacles will affect flow and speed levels. Travel time was shorter when participants were in the medium-flow rate experiments. In terms of the distance of each individual’s travel, our data showed that there was no significant difference in all the flow rate experiments for both speed levels. Our results also show that when the pedestrians crossed an obstacle, the lateral distance averaged from 0.3 m to 0.7 m, depending on the flow rate and speed level. We then explored how the body sways behaved while avoiding obstacles. It is observed that the average sway of the body was less in the high-speed conditions compared to the low-speed conditions – except for the HF & 4.8 m experiment. These results are expected to provide an insight into the characteristics of the behaviour of pedestrians when avoiding objects, and this could help enhance agent-based models.

scholarly journals Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

2021 ◽  
Vol 10 (3) ◽  
pp. 190
Author(s):  
Saeed Rahimi ◽  
Antoni B. Moore ◽  
Peter A. Whigham
Keyword(s):  
Conceptual Model ◽  
Movement Analysis ◽  
Autonomous Agent ◽  
Levels Of Abstraction ◽  
Agent Based ◽  
Cognitive Reasoning ◽  
Movement Data ◽  
Decision Making Processes ◽  
Underlying Causes ◽  
Tools And Techniques

Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This paper presents such a model in which three conceptual levels of abstraction are proposed to frame an agent-based representation of movement decision-making processes: ‘attribute,’ ‘actor,’ and ‘autonomous agent’. These in combination with three temporal, spatial, and spatiotemporal general forms of observations distinguish nine (3 × 3) representation typologies of movement data within the agent framework. Thirdly, there are three levels of cognitive reasoning: ‘association,’ ‘intervention,’ and ‘counterfactual’. This makes for 27 possible types of operation embedded in a conceptual cube with the level of abstraction, type of observation, and degree of cognitive reasoning forming the three axes. The conceptual model is an arena where movement queries and the statement of relevant objectives takes place. An example implementation of a tightly constrained spatiotemporal scenario to ground the agent-structure was summarised. The platform has been well-defined so as to accommodate different tools and techniques to drive causal inference in computational movement analysis as an immediate future step.

scholarly journals The Pandemics in Artificial Society: Agent-Based Model to Reflect Strategies on COVID-19

2020 ◽  
Author(s):  
Hokky Situngkir
Keyword(s):  
Social Interactions ◽  
Disease Transmission ◽  
Psychological Factors ◽  
Social Structures ◽  
Agent Based Model ◽  
Social Psychological ◽  
Agent Based ◽  
Policy Strategy ◽  
Artificial Society ◽  
Basic Ideas

Various social policies and strategies have been deliberated and used within many countries to handle the COVID-19 pandemic. Some of those basic ideas are strongly related to the understanding of human social interactions and the nature of disease transmission and spread. In this paper, we present an agent- based approach to model epidemiological phenomena as well as the interventions upon it. We elaborate on micro-social structures such as social-psychological factors and distributed ruling behaviors to grow an artificial society where the interactions among agents may exhibit the spreading of the virus. Capturing policies and strategies during the pandemic, four types of intervention are also applied in society. Emerged macro-properties of epidemics are delivered from sets of simulations, lead to comparisons between each effectivity of the policy/strategy.

Discovering and Evaluating Workflow Organizational Patterns from Events Log

2014 ◽  
Vol 6 (4) ◽  
pp. 19-34
Author(s):  
Mahdi Abdelkafi ◽  
Wassim Chtourou ◽  
Lotfi Bouzguenda
Keyword(s):  
Organizational Structures ◽  
Social Structures ◽  
Major Drawback ◽  
Agent Based ◽  
Workflow Mining ◽  
Activity Distribution ◽  
Interaction Protocols ◽  
Organizational Patterns

This paper contributes to address an important issue in Workflow mining: organizational patterns mining issue. First, it reveals a critical and comparative study of three representative Workflow mining systems (InWolve, WorkflowMiner and ProM). The major drawback of these systems is their inability to deal with organizational patterns mining. This work considers organizational patterns as being social structures defining the activity distribution among actors involved in the Workflow, as well as the interaction protocols ruling the communications between them. To compensate the previous drawback, the paper proposes an agent based approach that includes an Events Log model integrating the interactions among actors using a performative-based enrichment of Events Log. This paper also gives the principles of organizational patterns mining and shows how evaluate the quality of discovered patterns and notably the organizational structures in terms of flexibility, efficiency and robustness. Finally, it describes DiscoopFlow that implements a crisis case study to validate the contributions.

scholarly journals Technological and social networks of a pastoralist artificial society: agent-based modeling of mobility patterns

2021 ◽  
Author(s):  
Juan Miguel Rodriguez-Lopez ◽  
Meike Schickhoff ◽  
Shubhankar Sengupta ◽  
Jürgen Scheffran
Keyword(s):  
Social Networks ◽  
Land Tenure ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Social Structures ◽  
Mobility Patterns ◽  
Agent Based ◽  
Artificial Society ◽  
Linear Behavior ◽  
Non Linear

AbstractThis paper explores the advantages of simulation to raise the question of how digital and social networks affect the mobility in a pastoralist artificial society in the context of environmental degradation. We aim to explore mechanisms and develop scenarios, which are going to be validated through further research. We use a model of a simple pastoralist society in a world without borders to migration by adding the possibility of experiencing the effects of social structures (such as family and friends) and technological networks (e.g., social media). It appears obvious that pastoralist mobility depends on other dimensions as land tenure and traditional knowledge; however, isolating these two effects and experimenting in a simple society allow us to filter the multidimensionality of mobility decisions and concentrate on comparing scenarios in several different social structures and technological network combinations. The results show an expected behavior of more connection and more mobility, and a non-linear emergent behavior where pastoralists wait for a longer amount of time to mobilize when they interact using powerful social and technological networks. This occurs until they decide to move, and then, they mobilize more quickly and strongly than they did when communication was non-existent between them. The literature on migration explains this emergent non-linear behavior.

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