Evolving Collective Behavior in an Artificial Ecology
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Flocks and Crowds
Flocks and Crowds Flocks and crowds are other essential concepts we'll be exploring in this book. amount of realism to your simulation in just a few lines of code. Crowds can be a bit more complex, but we'll be exploring some of the powerful tools that come bundled with Unity to get the job done. In this chapter, we'll cover the following topics: Learning the history of flocks and herds Understanding the concepts behind flocks Flocking using the Unity concepts Flocking using the traditional algorithm Using realistic crowds the technology being the swarm of bats in Batman Returns in 1992, for which he won an and accurate, the algorithm is also very simple to understand and implement. [ 115 ] Understanding the concepts behind As with them to the real-life behaviors they model. As simple as it sounds, these concepts birds exhibit in nature, where a group of birds follow one another toward a common on the group. We've explored how singular agents can move and make decisions large groups of agents moving in unison while modeling unique movement in each Island. This demo came with Unity in Version 2.0, but has been removed since Unity 3.0. For our project. the way, you'll notice some differences and similarities, but there are three basic the algorithm's introduction in the 80s: Separation: This means to maintain a distance with other neighbors in the flock to avoid collision. The following diagram illustrates this concept: Here, the middle boid is shown moving in a direction away from the rest of the boids, without changing its heading [ 116 ] Alignment: This means to move in the same direction as the flock, and with the same velocity. -
Swarm Intelligence
Swarm Intelligence Leen-Kiat Soh Computer Science & Engineering University of Nebraska Lincoln, NE 68588-0115 [email protected] http://www.cse.unl.edu/agents Introduction • Swarm intelligence was originally used in the context of cellular robotic systems to describe the self-organization of simple mechanical agents through nearest-neighbor interaction • It was later extended to include “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies” • This includes the behaviors of certain ants, honeybees, wasps, cockroaches, beetles, caterpillars, and termites Introduction 2 • Many aspects of the collective activities of social insects, such as ants, are self-organizing • Complex group behavior emerges from the interactions of individuals who exhibit simple behaviors by themselves: finding food and building a nest • Self-organization come about from interactions based entirely on local information • Local decisions, global coherence • Emergent behaviors, self-organization Videos • https://www.youtube.com/watch?v=dDsmbwOrHJs • https://www.youtube.com/watch?v=QbUPfMXXQIY • https://www.youtube.com/watch?v=M028vafB0l8 Why Not Centralized Approach? • Requires that each agent interacts with every other agent • Do not possess (environmental) obstacle avoidance capabilities • Lead to irregular fragmentation and/or collapse • Unbounded (externally predetermined) forces are used for collision avoidance • Do not possess distributed tracking (or migration) -
A Complex Adaptive Systems Perspective to Appreciative Inquiry: a Theoretical Analysis Payam Saadat George Fox University
http://journals.sfu.ca/abr ADVANCES IN BUSINESS RESEARCH 2015, Volume 6, pages 1-13 A Complex Adaptive Systems Perspective to Appreciative Inquiry: A Theoretical Analysis Payam Saadat George Fox University Appreciate inquiry is utilized to facilitate organizational change by encouraging stakeholders to explore positives and generative capacities within their organization. In the literature, analysis of the effectiveness of AI is confined to psychological and managerial explanations such as highlighting the promotion of positive mindset and collective organizational planning. This paper will discuss a complex adaptive systems (CAS) perspective and present a new model for understanding the functionality of AI. The emphasis of this paper is placed on exploring the effects of AI on the behavior and interactions of agents/employees related to how they cope with change. An analysis of AI’s functionality through the lens of CAS reveals two critical insights: a) AI enhances adaptability to change by strengthening communication among agents, which in turn fosters the emergence of effective team arrangements and a more rapid collective response to change and b) AI possesses the potential to generate a collective memory for social systems within an organization. Furthermore, a systematic analysis of AI indicates a close connection between this method and CAS-based styles of management. This paper concludes by suggesting that AI might represent a potential method with the capacity to place organizational teams at the edge of chaos. Keywords: appreciative inquiry, organizational change, management, complex adaptive systems, edge of chaos Introduction In today’s fast-paced world where the fluctuating preferences of consumers, the growth in the global web of interdependence, and technological advancements guide the co-evolving relationship between the dominant business environment and the social systems within its domain, an organization’s capacity to cope with change in a timely manner determines its survival (Hesselbein & Goldsmith, 2006; Macready & Meyer, 1999; Senge, 2006). -
Chapter 17: Social Change and Collective Behavior
CHAPTER 17 SocialSocial ChangeChange andand CollectiveCollective BehaviorBehavior 566 U S Your Sections I Sociological N Imagination 1. Social Change G 2. Theoretical Perspectives on Social Change hen you see photos or films showing the Plains Indians of the 3. Collective Behavior WOld West—Sioux, Crow, and so forth—what do you think about the culture 4. Social Movements of those Native Americans? If you’re like most of us, you may assume that it had re- mained unchanged for many centuries—that these people dressed and acted in exactly Learning Objectives the same way as their ancestors. We often assume that nonindustrial soci- eties such as these stand still over time. After reading this chapter, you will be able to Actually, though, sociology teaches us that change comes to all societies. Whether by ❖ illustrate the three social processes that borrowing from other cultures, discovering contribute to social change. new ways of doing things, or creating inven- ❖ discuss how technology, population, nat- tions that ripple through society, all peoples ural environment, revolution, and war experience social change. cause cultures to change. Let’s return to the example of the Plains Indians. You may picture these tribes as ❖ describe social change as viewed by the fierce, buffalo-hunting warriors. Perhaps im- functionalist and conflict perspectives. ages of Sitting Bull and Crazy Horse astride ❖ discuss rumors, fads, and fashions. fast horses attacking Custer come to mind, ❖ compare and contrast theories of crowd leading you to think that their ancestors for centuries had also ridden horses. In fact, behavior. horses were a relatively recent introduction ❖ compare and contrast theories of social to Plains Indian culture in the 1800s. -
Collective Behaviour
COLLECTIVE BEHAVIOUR DEFINED COLLECTIVE BEHAVIOUR • The term "collective behavior" was first used by Robert E. Park, and employed definitively by Herbert Blumer, to refer to social processes and events which do not reflect existing social structure (laws, conventions, and institutions), but which emerge in a "spontaneous" way. Collective Behaviour defined • Collective behaviour is a meaning- creating social process in which new norms of behaviour that challenges conventional social action emerges. Examples of Collective Behaviour • Some examples of this type of behaviour include panics, crazes, hostile outbursts and social movements • Fads like hula hoop; crazes like Beatlemania; hostile outbursts like anti- war demonstrations; and Social Movements. • Some argue social movements are more sophisticated forms of collective behaviour SOCIAL MOVEMENTS • Social movements are a type of group action. They are large informal groupings of individuals and/or organizations focused on specific political or social issues, in other words, on carrying out, resisting or undoing a social change. CBs and SMs 19th C. ROOTS • Modern Western social movements became possible through education and the increased mobility of labour due to the industrialisation and urbanisation of 19th century societies Tilly’s DEFINITION SM • Charles Tilly defines social movements as a series of contentious performances, displays and campaigns by which ordinary people made collective claims on others [Tilly, 2004].]: Three major elements of SMs • For Tilly, social movements are a major vehicle for ordinary people's participation in public politics [Tilly, 2004:3]. • He argues that there are three major elements to a social movement [Tilly, 2004 THREE ELEMENTS OF SMs 1. Campaigns: a sustained, organized public effort making collective claims on target authorities; 2. -
Counter-Misdirection in Behavior-Based Multi-Robot Teams *
Counter-Misdirection in Behavior-based Multi-robot Teams * Shengkang Chen, Graduate Student Member, IEEE and Ronald C. Arkin, Fellow, IEEE Abstract—When teams of mobile robots are tasked with misdirection approach using a novel type of behavior-based different goals in a competitive environment, misdirection and robot agents: counter-misdirection agents (CMAs). counter-misdirection can provide significant advantages. Researchers have studied different misdirection methods but the II. BACKGROUND number of approaches on counter-misdirection for multi-robot A. Robot Deception systems is still limited. In this work, a novel counter-misdirection approach for behavior-based multi-robot teams is developed by Robotic deception can be interpreted as robots using deploying a new type of agent: counter misdirection agents motions and communication to convey false information or (CMAs). These agents can detect the misdirection process and conceal true information [4],[5]. Deceptive behaviors can have “push back” the misdirected agents collaboratively to stop the a wide range of applications from military [6] to sports [7]. misdirection process. This approach has been implemented not only in simulation for various conditions, but also on a physical Researchers have studied robotic deception previously [4] robotic testbed to study its effectiveness. It shows that this through computational approaches [8] in a wide range of areas approach can stop the misdirection process effectively with a from human–robot interaction (HRI) [9]–[11] to ethical issues sufficient number of CMAs. This novel counter-misdirection [4]. Shim and Arkin [5] proposed a taxonomy of robot approach can potentially be applied to different competitive deception with three basic dimensions: interaction object, scenarios such as military and sports applications. -
Boids Algorithm in Economics and Finance a Lesson from Computational Biology
University of Amsterdam Faculty of Economics and Business Master's thesis Boids Algorithm in Economics and Finance A Lesson from Computational Biology Author: Pavel Dvoˇr´ak Supervisor: Cars Hommes Second reader: Isabelle Salle Academic Year: 2013/2014 Declaration of Authorship The author hereby declares that he compiled this thesis independently, using only the listed resources and literature. The author also declares that he has not used this thesis to acquire another academic degree. The author grants permission to University of Amsterdam to reproduce and to distribute copies of this thesis document in whole or in part. Amsterdam, July 18, 2014 Signature Bibliographic entry Dvorˇak´ , P. (2014): \Boids Algorithm in Economics and Finance: A Les- son from Computational Biology." (Unpublished master's thesis). Uni- versity of Amsterdam. Supervisor: Cars Hommes. Abstract The main objective of this thesis is to introduce an ABM that would contribute to the existing ABM literature on modelling expectations and decision making of economic agents. We propose three different models that are based on the boids model, which was originally designed in biology to model flocking be- haviour of birds. We measure the performance of our models by their ability to replicate selected stylized facts of the financial markets, especially those of the stock returns: no autocorrelation, fat tails and negative skewness, non- Gaussian distribution, volatility clustering, and long-range dependence of the returns. We conclude that our boids-derived models can replicate most of the listed stylized facts but, in some cases, are more complicated than other peer ABMs. Nevertheless, the flexibility and spatial dimension of the boids model can be advantageous in economic modelling in other fields, namely in ecological or urban economics. -
Animating Predator and Prey Fish Interactions
Georgia State University ScholarWorks @ Georgia State University Computer Science Dissertations Department of Computer Science 5-6-2019 Animating Predator and Prey Fish Interactions Sahithi Podila Follow this and additional works at: https://scholarworks.gsu.edu/cs_diss Recommended Citation Podila, Sahithi, "Animating Predator and Prey Fish Interactions." Dissertation, Georgia State University, 2019. https://scholarworks.gsu.edu/cs_diss/149 This Dissertation is brought to you for free and open access by the Department of Computer Science at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Computer Science Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. ANIMATING PREDATOR AND PREY FISH INTERACTIONS by SAHITHI PODILA Under the Direction of Ying Zhu, PhD ABSTRACT Schooling behavior is one of the most salient social and group activities among fish. They form schools for social reasons like foraging, mating and escaping from predators. Animating a school of fish is difficult because they are large in number, often swim in distinctive patterns that is they take the shape of long thin lines, squares, ovals or amoeboid and exhibit complex coordinated patterns especially when they are attacked by a predator. Previous work in computer graphics has not provided satisfactory models to simulate the many distinctive interactions between a school of prey fish and their predator, how does a predator pick its target? and how does a school of fish react to such attacks? This dissertation presents a method to simulate interactions between prey fish and predator fish in the 3D world based on the biological research findings. -
Flocks, Herds, and Schools: a Distributed Behavioral Model 1
Published in Computer Graphics, 21(4), July 1987, pp. 25-34. (ACM SIGGRAPH '87 Conference Proceedings, Anaheim, California, July 1987.) Flocks, Herds, and Schools: A Distributed Behavioral Model 1 Craig W. Reynolds Symbolics Graphics Division [obsolete addresses removed 2] Abstract The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle system, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds. Categories and Subject Descriptors: 1.2.10 [Artificial Intelligence]: Vision and Scene Understanding; 1.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; 1.3.7 [Computer Graphics]: Three Dimensional Graphics and Realism-Animation: 1.6.3 [Simulation and Modeling]: Applications. General Terms: Algorithms, design.b Additional Key Words, and Phrases: flock, herd, school, bird, fish, aggregate motion, particle system, actor, flight, behavioral animation, constraints, path planning. -
Expert Assessment of Stigmergy: a Report for the Department of National Defence
Expert Assessment of Stigmergy: A Report for the Department of National Defence Contract No. W7714-040899/003/SV File No. 011 sv.W7714-040899 Client Reference No.: W7714-4-0899 Requisition No. W7714-040899 Contact Info. Tony White Associate Professor School of Computer Science Room 5302 Herzberg Building Carleton University 1125 Colonel By Drive Ottawa, Ontario K1S 5B6 (Office) 613-520-2600 x2208 (Cell) 613-612-2708 [email protected] http://www.scs.carleton.ca/~arpwhite Expert Assessment of Stigmergy Abstract This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarm- based problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described. -
The Emergence of Animal Social Complexity
The Emergence of Animal Social Complexity: theoretical and biobehavioral evidence Bradly Alicea Orthogonal Research (http://orthogonal-research.tumblr.com) Keywords: Social Complexity, Sociogenomics, Neuroendocrinology, Metastability AbstractAbstract ThisThis paper paper will will introduce introduce a a theorytheory ofof emergentemergent animalanimal socialsocial complexitycomplexity usingusing variousvarious results results from from computational computational models models andand empiricalempirical resultsresults.. TheseThese resultsresults willwill bebe organizedorganized into into a avertical vertical model model of of socialsocial complexity.complexity. ThisThis willwill supportsupport thethe perspperspectiveective thatthat social social complexity complexity is isin in essence essence an an emergent emergent phenomenon phenomenon while while helping helping to answerto answer of analysistwo interrelated larger than questions. the individual The first organism.of these involves The second how behavior involves is placingintegrated aggregate at units socialof analysis events larger into thethan context the individual of processes organism. occurring The secondwithin involvesindividual placing organi aggregatesms over timesocial (e.g. events genomic into the and context physiological of processes processes) occurring. withinBy using individual a complex organisms systems over perspective,time (e.g. fivegenomic principles and physiologicalof social complexity processes). can Bybe identified.using a complexThese principles systems -
Threshold Models of Collective Behavior!
Threshold Models of Collective Behavior! Mark Granovetter State University of New York at Stony Brook Models of collective behavior are developed for situations where actors have two alternatives and the costs and/or benefits of each depend on how many other actors choose which alternative. The key concept is that of "threshold": the number or proportion of others who must make one decision before a given actor does so; this is the point where net benefits begin to exceed net costs for that particular actor. Beginning with a frequency distribution of thresholds, the models allow calculation of the ultimate or "equilibrium" number making each decision. The stability of equilibrium results against various possible changes in threshold distributions is considered. Stress is placed on the importance of exact distributions for outcomes. Groups with similar average preferences may generate very different results; hence it is hazardous to infer individual dispositions from aggregate outcomes or to assume that behavior was directed by ultimately agreed-upon norms. Suggested applications are to riot behavior, innovation and rumor diffusion, strikes, voting, and migra tion. Issues of measurement, falsification, and verification are dis cussed. BACKGROUND AND DESCRIPTION OF THE MODELS Because sociological theory tends to explain behavior by institutionalized norms and values, the study of behavior inexplicable in this way occupies a peripheral position in systematic theory. Work in the subfields which embody this concern-deviance for individuals and collective behavior for groups-often consists of attempts to show what prevented the established patterns from exerting their usual sway. In the field of collective behavior, one such effort involves the assertion that new norms or beliefs "emerge" 1 This report is heavily indebted to three co-workers.