Evolutionary Economics and Social Complexity Science 19 Stanislaw Raczynski Interacting Complexities of Herds and Social Organizations Agent Based Modeling Evolutionary Economics and Social Complexity Science Volume 19 Editors-in-Chief Takahiro Fujimoto, Tokyo, Japan Yuji Aruka, Tokyo, Japan Editorial Board Satoshi Sechiyama, Kyoto, Japan Yoshinori Shiozawa, Osaka, Japan Kiichiro Yagi, Neyagawa, Osaka, Japan Kazuo Yoshida, Kyoto, Japan Hideaki Aoyama, Kyoto, Japan Hiroshi Deguchi, Yokohama, Japan Makoto Nishibe, Sapporo, Japan Takashi Hashimoto, Nomi, Japan Masaaki Yoshida, Kawasaki, Japan Tamotsu Onozaki, Tokyo, Japan Shu-Heng Chen, Taipei, Taiwan Dirk Helbing, Zurich, Switzerland The Japanese Association for Evolutionary Economics (JAFEE) always has adhered to its original aim of taking an explicit "integrated" approach This path has been followed steadfastly since the Association’s establishment in 1997 and, as well, since the inauguration of our international journal in 2004 We have deployed an agenda encompassing a contemporary array of subjects including but not limited to: foundations of institutional and evolutionary economics, criticism of mainstream views in the social sciences, knowledge and learning in socio-economic life, development and innovation of technologies, transformation of industrial organizations and economic systems, experimental studies in economics, agent- based modeling of socio-economic systems, evolution of the governance structure of firms and other organizations, comparison of dynamically changing institutions of the world, and policy proposals in the transformational process of economic life In short, our starting point is an "integrative science" of evolutionary and institutional views Furthermore, we always endeavor to stay abreast of newly established methods such as agent-based modeling, socio/econo-physics, and network analysis as part of our integrative links More fundamentally, “evolution” in social science is interpreted as an essential key word, i.e., an integrative and /or communicative link to understand and re-domain various preceding dichotomies in the sciences: ontological or epistemological, subjective or objective, homogeneous or heterogeneous, natural or artificial, selfish or altruistic, individualistic or collective, rational or irrational, axiomatic or psychologicalbased, causal nexus or cyclic networked, optimal or adaptive, micro- or macroscopic, deterministic or stochastic, historical or theoretical, mathematical or computational, experimental or empirical, agent-based or socio/econo-physical, institutional or evolutionary, regional or global, and so on The conventional meanings adhering to various traditional dichotomies may be more or less obsolete, to be replaced with more current ones vis-à-vis contemporary academic trends Thus we are strongly encouraged to integrate some of the conventional dichotomies These attempts are not limited to the field of economic sciences, including management sciences, but also include social science in general In that way, understanding the social profiles of complex science may then be within our reach In the meantime, contemporary society appears to be evolving into a newly emerging phase, chiefly characterized by an information and communication technology (ICT) mode of production and a service network system replacing the earlier established factory system with a new one that is suited to actual observations In the face of these changes we are urgently compelled to explore a set of new properties for a new socio/economic system by implementing new ideas We thus are keen to look for “integrated principles” common to the above-mentioned dichotomies throughout our serial compilation of publications We are also encouraged to create a new, broader spectrum for establishing a specific method positively integrated in our own original way More information about this series at http://www.springer.com/series/11930 Stanislaw Raczynski Interacting Complexities of Herds and Social Organizations Agent Based Modeling Stanislaw Raczynski Facultad de Ingeniería Universidad Panamericana Ciudad de México, México ISSN 2198-4204 ISSN 2198-4212 (electronic) Evolutionary Economics and Social Complexity Science ISBN 978-981-13-9336-5 ISBN 978-981-13-9337-2 (eBook) https://doi.org/10.1007/978-981-13-9337-2 © Springer Nature Singapore Pte Ltd 2020 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface According to John von Neumann, “by a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed phenomena The justification of such a mathematical construct is solely and precisely that it is expected to work — that is, correctly to describe phenomena from a reasonably wide area.” Humans always (sometimes unconsciously) have used models created in their brains When our technical skills have grown, the models acquired the form of physical, scale models, drawings, and finally sophisticated logical and mathematical constructions The common concept of modeling is defined as a scientific activity, the aim of which is to make a particular part or feature of the world easier to understand The complexity of the real world can be modeled to some extent There are many definitions of complexity, recently related to “system of systems” structures Note that a system that contains a great number of sub-systems or items or a huge number of differential equations is not necessarily complex The complexity lies in the way the components interact with each other and the diversity of system components In such systems, the simulation results may provide information about the behavior of the whole system, which is not the sum of individual behavior patterns This is also interpreted as nonlinearity This book is focused on this kind of modeling and simulation experiments Analog and digital computers gave us a powerful tool for model building and analysis At the very beginning of the computer era, the differential equations have been solved on analog machines, helping scientists and engineers to design mechanisms, circuits, and complex devices The field of model applications has grown over the decades, including not only the works of engineering and exact sciences but also the models of animal and human societies At the very beginning, model builders have been looking for some kinds of algebraic, ordinary, or partial differential equations to describe real system behavior The most known and explored field is the System Dynamics (SD) approach that mainly uses models in the form of ordinary differential equations However, it should be noted that this is not the only way to build models A strange conviction aroused among the modelers that everything in the real world can be described by v vi Preface differential equations In general, this is not true Although the SD methodology is still widely used and useful, there are other ways for model building, like fuzzy logic, differential inclusions, discrete event simulation, and agent based models, among others The topic of this book is agent based modeling The rapid growth of the computational capacity of new computers permits us to create thousands of objects in computer memory and make them interact with each other In agent based models, the objects are equipped with certain artificial intelligence, can optimize their behavior, and take decisions Some systems can be modeled both using differential equations and agent based approach The results of these two methods are frequently quite different, for example, results of the Lotka-Volterra prey-predator model and the prey-predator agent based model Here, we will not suggest which of these models is valid or not These are just different modeling methods that produce results of different kind Undoubtedly, agent based modeling is more flexible and can reflect more behavioral patterns of the individuals, providing the insight on the macro- behavior of the system In Chap 1, there are comments on some agent based modeling tools The other chapters contain examples of applications to artificial societies and competing populations of individuals and the growth, interactions, and decay of organizations and other applications For reader’s convenience, a short recall about object- and agent-based modeling is repeated in each chapter Thus, each chapter can be read as independent unit In Chap 9, you can find a description of an experimental software package that uses the classic continuous system dynamics graphical user interface (GUI) that is used to construct the model However, the transparent simulation engine that runs behind this GUI is discrete event simulation This way, we can compare the results of the conventional system dynamics packages with these provided by discrete event simulation The relevant differences between these two simulation paradigms are pointed out Mexico City, Mexico Stanislaw Raczynski Acknowledgements I would like to express my gratitude to the Editors of the journals listed below for the permission to use the updated versions of my articles, as follows: Simulating self-organization and interference between certain hierarchical structures Nonlinear Dynamics, Psychology, and Life Sciences, 2014, Vol 18, no 4, used in Chap of this book, A Self-destruction game, Nonlinear Dynamics, Psychology, and Life Sciences, 2006, Vol 10, no 4, used in Chap of this book, The spontaneous rise of the herd instinct: agent-based simulation, Nonlinear Dynamics, Psychology, and Life Sciences, to appear, used in Chap of this book Simulation of the dynamic interactions between terror and anti-terror organizational structures, Journal of Artificial Societies and Social Simulation, Vol 7, no 2, used in Chap of this book Influence of the gregarious instinct and individuals’ behavior patterns on macro migrations: simulation experiments, Journal of Human Behavior in the Social Environment, Vol 28, no 2, used in Chap of this book Visit the journal home page at www.tandfonline.com Stanislaw Raczynski vii Contents 1 Agent-Based Models: Tools ���������������������������������������������������������������������� 1 1.1 General Remarks������������������������������������������������������������������������������ 1 1.2 Discrete Event Simulation���������������������������������������������������������������� 2 1.2.1 GPSS ������������������������������������������������������������������������������������ 4 1.2.2 Arena������������������������������������������������������������������������������������ 4 1.2.3 SIMIO ���������������������������������������������������������������������������������� 5 1.2.4 Simula ���������������������������������������������������������������������������������� 5 1.2.5 PASION, PSM++, and BLUESSS���������������������������������������� 6 1.3 Example�������������������������������������������������������������������������������������������� 12 1.4 Conclusion���������������������������������������������������������������������������������������� 16 References�������������������������������������������������������������������������������������������������� 16 2 Simulating Self-Organization and Interference Between Certain Hierarchical Structures ���������������������������������������������������������������������������� 19 2.1 Introduction�������������������������������������������������������������������������������������� 19 2.2 The Model���������������������������������������������������������������������������������������� 21 2.2.1 General Concepts������������������������������������������������������������������ 21 2.2.2 Interaction Rules ������������������������������������������������������������������ 23 2.3 Simulation ���������������������������������������������������������������������������������������� 25 2.4 Conclusion���������������������������������������������������������������������������������������� 27 References�������������������������������������������������������������������������������������������������� 28 3 Interactions Between Terror and Anti-terror Organizations���������������� 31 3.1 Introduction�������������������������������������������������������������������������������������� 31 3.2 The Model���������������������������������������������������������������������������������������� 33 3.2.1 Interactions Between Structures ������������������������������������������ 36 3.2.2 Simulation Tool and Model Implementation������������������������ 37 3.2.3 Simulation Experiments�������������������������������������������������������� 40 3.3 Conclusion���������������������������������������������������������������������������������������� 45 References�������������������������������������������������������������������������������������������������� 45 ix x Contents 4 Organization Growth and Decay: Simulating Interactions of Hierarchical Structures, Corruption and Gregarious Effect������������ 47 4.1 Introduction�������������������������������������������������������������������������������������� 47 4.2 Agent-Based Modeling �������������������������������������������������������������������� 49 4.3 Simulation Tool�������������������������������������������������������������������������������� 51 4.4 The Model���������������������������������������������������������������������������������������� 52 4.4.1 The Individuals �������������������������������������������������������������������� 52 4.4.2 Organizations������������������������������������������������������������������������ 54 4.4.3 Auxiliary Control Process���������������������������������������������������� 55 4.5 Simulation Experiments�������������������������������������������������������������������� 55 4.5.1 Experiment 1: Criterion Function Zero�������������������������������� 57 4.5.2 Experiment 2: Change Criterion – Size�������������������������������� 58 4.5.3 Experiment 3: Corruption Level ������������������������������������������ 59 4.5.4 Experiment 4: Accumulated Corruption ������������������������������ 59 4.5.5 Experiment 5: Criterion – Grow Rate (Herd Instinct)���������� 61 4.6 Conclusion���������������������������������������������������������������������������������������� 63 References�������������������������������������������������������������������������������������������������� 63 5 The Spontaneous Rise of the Herd Instinct: Agent-Based Simulation�������������������������������������������������������������������������������������������������� 67 5.1 Introduction�������������������������������������������������������������������������������������� 67 5.2 Agent-Based Modeling �������������������������������������������������������������������� 69 5.2.1 General Remarks������������������������������������������������������������������ 69 5.2.2 BLUESSS Simulation Package�������������������������������������������� 70 5.3 The Model���������������������������������������������������������������������������������������� 71 5.3.1 Environment�������������������������������������������������������������������������� 71 5.3.2 Event: Search for Food �������������������������������������������������������� 73 5.4 Simulations �������������������������������������������������������������������������������������� 75 5.4.1 Gregarious Factor, Search for Food�������������������������������������� 75 5.4.2 The Influence of the Threat�������������������������������������������������� 76 5.5 Conclusion���������������������������������������������������������������������������������������� 79 Appendix���������������������������������������������������������������������������������������������������� 80 References�������������������������������������������������������������������������������������������������� 81 6 Influence of the Gregarious Instinct and Individuals’ Behavior Patterns on Macro Migrations: Simulation Experiments���������������������� 83 6.1 Introduction�������������������������������������������������������������������������������������� 83 6.2 Object- and Agent-Based Models ���������������������������������������������������� 84 6.3 The Simulation Tool�������������������������������������������������������������������������� 85 6.4 The Model���������������������������������������������������������������������������������������� 86 6.5 Simulations �������������������������������������������������������������������������������������� 89 6.6 Similarity to the Real Data���������������������������������������������������������������� 94 6.7 Conclusion���������������������������������������������������������������������������������������� 95 References�������������������������������������������������������������������������������������������������� 96 134 9 Discrete Event Simulation vs Continuous System Dynamics Note that the continuous models of discrete event systems, like the birth-and- death equation, manage the expected values as dependent variables To use this kind of models, we must be sure that the expected value does exist This means that we need xf(x) to be integrable on R, in the sense of Lebesgue, where f(x) is the probability density function So, such density function must exist However, in the real world, it is not always true If a model component has an uncertain value, it does not mean that it is a random value with existing probabilistic properties Consult Aubin and Cellina (1984) and Aubin et al (2014) for the discussion of tychastic variables that are not random and may have no density function Models that include tychastic variables can hardly be treated using the differential equation models and the system dynamics methods The method presented here should not be misunderstood as simulation of queuing models and discrete operations like a job shop The aim of our approach is to use the classical SD systems thinking and its graphical model representation (levels, flows, functions) However, there are no differential equations behind the model The flows are ways where discrete entities move, and not the derivatives of the corresponding levels A new software tool for this methodology is presented There exists a large class of dynamic systems that can be modeled using differential equations, like mechanical systems, electric circuits, and other physical systems The fast development of the simulation tools for continuous system simulation was perhaps a cause for the temptation to extend these models of this type to “soft system” modeling, including demographic growth; industrial dynamics; urban, ecological, and social systems; and nearly everything On the other hand, discrete event simulation is also widely applied to such kind of models One could expect that the results obtained from both methodologies should be the same However, it is not always the case The discrete event approach to system dynamics is not new What we pretend here is rather to present a tool which is closer related with the GUI of the classical system dynamics (SD, Forrester, (1961)), preserving the concepts of levels, flows, and functions, so that the models of SD could be easily ported to the discrete event system dynamics (DESD) environment A similar tool and methodology, based on Java, can be found in the publications of Borshchev (2001) and Borshchev and Filippov (2004), who have done significant contributions in the field In that paper, we can find a comparison between the classical SD continuous simulation of the prey-predator system (Lotka-Volterra equations, Volterra 1926) and the discrete event model of the same problem The solutions seem to be similar, but not equal to each other Moreover, in the discrete event and agent-oriented simulation, the spatial distribution of the objects can be simulated This is hardly possible while using the continuous SD approach Other authors also observed the spatial behavior of the two populations A comparison of SD and discrete event simulation (DES) can be found in Tako and Robinson (2007) For more references, see the journal Discrete Event System Dynamics by Xi-Ren, Cao (Editor), and Brailsford and Hilton (2001) Some general remarks on discrete versus continuous simulation can be found in Raczynski (2006a, b) A new software tool for this methodology is presented here 9.2 The DESD Tool 135 9.2 The DESD Tool Our DESD tool needs the BLUESSS package (Blues Simulation System; see http:// www.raczynski.com/pn/bluesss.htm) and the C++Builder of Borland DESD generates the BLUESSS code that is being translated too C++ and run using the C++Builder Recall that the main SD variables are levels (e.g., the number of individuals in a population), flows (level change rates), and functions Simulating a simple birth- and-death problem, we obtain, as a result, the levels (deterministic number of individuals) as functions of time However, the real system is not deterministic So, what represents the level? Is it the expected value, the most probable value, or other statistics? These variables are not equal to each other Few SD users care about this The other question is the birth process (in the birth-deaths models), which is almost always supposed to be a Poisson process, and the death rate In the classical SD, the death rate is a function of the population level and eventually of other variables In fact, this rate is not always a function of the levels It is the result of the death process, which depends on the (random) lifetime of each individual Real systems with many individuals can have a huge memory (see, e.g., the immunological systems), which can hardly be modeled by a simple SD models, even with a big number of equations DESD approach, being rather behavioral and agent-oriented, can reflect the above system properties One can simulate similar models using other discrete event tools Our aim is rather to create a program that uses the classical SD model graphical representation and the user interface similar to that of existing DS packages The model structure can be defined in the exactly same way as in Stella or Powersim The model parameters are similar to an SD model but somewhat different For example, the birth rate is defined through the inter-arrival time, which is not a single number but a set of properties of the corresponding probability distribution Another set of data are the entity attributes Behind the model structure and data, there is a discrete event, object-oriented simulation engine As DESD generates the source BLUESSS code, the user can edit the code, or generate the C++ code and work on it This permits to define extra properties and behavior of the individual objects This post-processing of the model is optional; after defining the model structure and its basic parameters, the model can be run automatically, like a classical SD task The level block is not a simple number (integral of in/out flows), but it is a set of entities equipped with entity attributes These entities can be equal to each other or different Any flow or model function can depend not only on the level values and other functions but also on the attributes of the entities There are some important differences between the flow parameters of SD and DESD. In the simple SD population growth model, the flows are rates (entities per time unit), so the birth rate may be equal to bx where b is a constant and x is the actual population, and the death rate is equal to bx, b being a constant For a model of telephone exchange, the birth rate is a constant (does not depend on the number of active calls) In DESD, the birth process is described by the inter-arrival time, 136 9 Discrete Event Simulation vs Continuous System Dynamics roughly speaking prob(1/(bx)), prob being a random number generator However, the death flow is defined through the entity lifetime, so this parameter is rather a property of an individual entity, and it is not proportional to 1/x 9.3 Examples 9.3.1 A Simple Birth-Death Process Figure 9.1 shows a model of population growth This is a classical SD scheme, created by the SimBall SD program (http://www raczynski.com/pn/simball.htm) The same can be done with any other SD tool The birth rate of the rabbits is equal to 3R where R is the number of rabbits (level “total rabbits”) Suppose that the initial deaths rate is slightly less than the births rate This means that the population growth is exponential Now suppose that after some initial time interval, the mortality increases for some reason, for example, food shortage or presence of predators This factor is defined in the time function f that influences the death rate The model equations used in the simulation are as follows (model 3.1): dx/dt = b−d b = 3x d = 2.85x(1 + 0.0001 f), where f = 0 for t less or equal to 30 f = t−30 for t greater than 30 x number of rabbits, b birth rate, d death rate, t model time As expected, the SD continuous simulation shows initial exponential growth and then a decay The graph of x(t) is smooth and nice, as shown in Fig. 9.2 Now we simulate the same model with DESD. The model parameters are similar To use DESD, we must define the distribution of the inter-arrival times and of the entity lifetime Suppose that the input flow is of Poisson type, with exponential distribution Consider two cases: (1) the distribution of the lifetime to be exponential also and (2) the lifetime has the Erlang distribution of order 3, with the same mean value The results are shown in Fig. 9.3 Fig 9.1 SD model of a simple birth-death process 9.3 Examples 137 Fig 9.2 Population of rabbits Continuous SD model Fig 9.3 Rabbits population, DESD model 3.1 Plot (a) exponential lifetime distribution, (b) Erlang distribution 138 9 Discrete Event Simulation vs Continuous System Dynamics The main difference is the irregular shape of the curves Of course, with greater number of rabbits, the plots become more regular, but the fluctuations always persist This is the user choice to accept continuous and nice SD solution, or the DESD irregular, but perhaps more realistic one However, this is not the only difference Observe that in the case A of Fig. 9.2 (negative exponential lifetime distribution), the curve does not approach zero so quickly as the SD solution Moreover, the lifetime distribution influences significantly the results, which cannot be shown using the SD model The results shown in Fig. 9.3 show the difference in the maximal size of the population, as well as the shape of the curves Using DESD we can perform a postmortem variance analysis The results are shown below These are confidence intervals for the model trajectories in both cases With probability of 0.90, the solution remains in the shadowed region The line inside the shadowed area is the averaged trajectory The analysis was made over 100 repetitions of the simulation Observe that in the case of the exponential distribution, the average trajectory reaches approximately 1719, and for Erlang distribution, this value is equal to 4813 (Fig. 9.4) There is also a significant difference in the shape of the plots in the two cases, in particular the magnitude of the plot tail These model properties can hardly be seen using SD simulation Of course, we can introduce some random noise into the SD equations, but it is not the same as managing the entity lifetime and between-arrival distribution The plot below shows the probability distribution of the number of rabbits in the Erlang case The horizontal axis is the model time, the axis toward the viewer is the total of rabbits, and the vertical coordinate is the corresponding probability Figure 9.5 depicts the 3D image of the probability density function for this model The horizontal axes are rabbit population and time; vertical axis is the probability density function 9.3.2 Prey-Predator Model The scheme of Fig. 9.6 represents a classical prey-predator model The birth rate of rabbits depends on the total of rabbits only The rabbit’s death rate is the function of the number of rabbits and of the number of wolves As for the wolves, their birth rate depends on the number of rabbits and the number of wolves (they reproduce faster when more food is available), and their death rate does not depend on rabbits (only natural deaths are considered) This is a simplified, widely known prey- predator model We will not discuss here the classical SD simulation, which results in non-sinusoidal oscillating trajectories The DESD model has exactly the same scheme The only difference is that the link between the levels and the corresponding death flow is not used in DESD, because these flows are defined by the individual lifetime distributions and the total output flow is the result of the death event execution (and not a given parameter) 9.3 Examples 139 Fig 9.4 Confidence intervals for model 3.1 The trajectory is included in the gray area with probability equal to 0.9, the line inside indicates the average trajectory The DESD simulation trajectories are also oscillating but reveal a high variance (Fig. 9.7) The upper curve is the number of rabbits, and the lower one is the number of wolves As in the previous example, the between-birth times were exponential, and the lifetime distribution was Erlang The results are quite different from the classical Lotka-Volterra solution This kind of simulation seems to be much more realistic Fig 9.5 Probability density function: 3D image Fig 9.6 SD model of a prey-predator system Fig 9.7 Prey-predator model (a) Rabbits, (b) wolves References 141 9.4 Conclusion The systems thinking (in the classical SD sense) is something more general than the continuous DYNAMO-like modeling It can be implemented using differential (or difference) equations, as well as discrete event simulation The aim of this chapter is not to discover this already known fact We rather propose a tool that uses the classical SD “level flow” scheme to construct the model structure but runs the simulation in discrete event mode The traditional division of M&S into continuous and discrete approach seems to be artificial The real systems are not exactly continuous or discrete This division is useful while selecting simulation software but seems to be too simplified from the point of view of methodology The future software development should result in simulation tools that use discrete/continuous approach The ideal solution may be a general purpose package where the simulation engine is transparent to the user and defines automatically simulation mode (discrete/continuous/combined), due to the model structure and available parameters References Aubin JP, Cellina A (1984) Differential inclusions Springer, Berlin https://doi.org/10.1007/9783-642-69512-4 ISBN: 978-3-642-69514-8 Aubin JP, Chen L, Dordan O (2014) Tychastic measure of viability risk Springer https://doi org/10.1007/978-3-319-08129-8 Borshchev A (2001) AnyLogic 4.0: simulating hybrid systems with extended UML-RT. Simul News Eur 31:15–16 Borshchev A, Filippov A (2004) From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools Conference paper: The 22nd international conference of the system dynamics society, Oxford, England Brailsford S, Hilton N (2001) A comparison of discrete event simulation and system dynamics for modelling healthcare systems Conference paper: proceedings of the 26th meeting of the ORAHS, Working Group 2000 Glasgow Caledonian University, Glasgow Raczynski S (2006a) In: Bargiela A (ed) Modeling and simulation: computer science of illusion Wiley, Chichester Raczynski S (2006b) A self-destruction game J Nonlinear Dyn Psychol Life Sci 10(4):471–483 ISBN/ISSN 1090-0578 Tako AA, Robinson S (2007) Comparing the use of discrete-event simulation and system dynamics models Conference paper: 2007 Winter Simulation Conference, Washington, DC Volterra V (1926) Variazioni e fluttuazioni del numero d’individui in specie animali conviventi References Adamic L, Adar E (2005) How to search a social network Soc Netw 27(3):187–203 Aubin JP, Cellina A (1984) Differential inclusions Springer, Berlin https://doi.org/10.1007/9783-642-69512-4 ISBN: 978-3-642-69514-8 Aubin JP, Chen L, Dordan O (2014) Tychastic measure of viability risk Springer https://doi org/10.1007/978-3-319-08129-8 Axelrod R (1997) The complexity of cooperation: agent-based models of competition and collaboration Princeton University Press, Princeton Ayala FJ, Gilpin ME, Ehrenfeld JG (1973) Competition between species: theoretical models and experimental tests Theor Popul Biol Elsevier 4(3):331–356 https://doi org/10.1016/0040-5809(73)90014-2 Bak P (1997) How nature works: the science of self-organized criticality Oxford University Press, Oxford Bandini S, Manzoni S, Vizzan G (2009) Agent based Modeling and simulation: an informatics perspective J Artif Soc Soc Simul 12(4):4 ISBN/ISSN 1460–7425 Basener W, Basener B, Radin M, Wiandt T (2008) Dynamics of a discrete population model for extinction and sustainability in ancient civilizations Nonlinear Dyn Psychol Life Sci 12(1):29–53 Bazzan A, Klugl F (2009) Multi-agent systems for traffic and transportation engineering Information Science Reference https://pdfs.semanticscholar.org/c22b/f7de730c0ab4e2e4ae6a66797a4186407de7.pdf ISBN/ISSN 978-1-60566-227-5 Blanchard OJ, Katz LF (1992) Regional evolutions Brook Pap Econ Act 1:1–37 Bommel P, Becu N, Le Page C, Bousquet F (2015) Cormas, an agent-based simulation platform for coupling human decisions with computerized dynamics In: Hybrid simulation and gaming in the network society series Translational Systems Sciences Springer, Singapore doi:https:// doi.org/10.1007/978-981-10-0575-6_27 Borshchev A (2001) AnyLogic 4.0: simulating hybrid systems with extended UML-RT. Simul News Eur 31:15–16 Borshchev A, Filippov (2004) AnyLogic – multi-paradigm simulation for business, engineering and research Conference paper: The 6th IIE annual simulation solutions conference, March 15–16, Orlando, Florida, USA Borshchev A, Filippov A (2004) From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools Conference paper: The 22nd international conference of the system dynamics society, Oxford, UK © Springer Nature Singapore Pte Ltd 2020 S Raczynski, Interacting Complexities of Herds and Social Organizations, Evolutionary Economics and Social Complexity Science 19, https://doi.org/10.1007/978-981-13-9337-2 143 144 References Brailsford S, Hilton N (2001) A comparison of discrete event simulation and system dynamics for modelling healthcare systems Conference paper: proceedings of the 26th meeting of the ORAHS, Working Group 2000 Glasgow Caledonian University, Glasgow Cecconi F, Parisi D (1998) Individual versus social survival strategies J Artif Soc Soc Simul 1(2):1–17 Chatterjee S, Seneta E (1977) Towards consensus: some convergence theorems on repeated averaging J Appl Probab 14(1):89–97 Chen G (2004) Stability of nonlinear systems In: Encyclopedia of RF and microwave engineering Wiley, New York Cioffi-Revilla C (1998) Politics and uncertainty: theory, models and applications Cambridge University Press, Cambridge Coakley S, Smallwood R, Holcombe M (2006) From molecules to insect communities – how formal agent based computational modeling is undercovering new biological facts http://www jams.or.jp/scm/contents/e-2006-7/2006-69.pdf , Scientiae Mathematicae Japonicae Online, e-2006, 765–778 Coakley S, Gheorghe M, Holcombe M et al (2012) Exploitation of high performance computing in the FLAME agent-based simulation framework Conference paper: IEEE 14th international conference on high performance computing and communications Cohen JE, Hajnal J, Newman CM (1986) Approaching consensus can be delicate when positions harden Stoch Process Appl 22(2):315–322 Cropp RA, Norbury J (2015) Population interactions in ecology: a rule-based approach to modeling ecosystems in a mass-conserving framework SIAM REVIEW, Soc Ind Appl Math 57(3):437–465 Crowder RM, Robinson MA, Hughes HPN, Sim YW (2012) The development of an agent-based modeling framework for simulating engineering team work IEEE Trans Syst Man Cybern Part A Syst 42(6):1426–1439 Daft RL (2013) Organization theory and design South Western Cengage Learning ISBN: 13:978-1-111-22129-4 Dahl O, Nygaard B (1967) Simula – an Algol-based simulation language Commun ACM 9:671–678 Deffuant G, Amblard F, Weisbuch G, Faure T (2002) How can extremism prevail? A study based on the relative agreement interaction model J Artif Soc Soc Simul 5(4) Edmonds B (2012) Context in social simulation: why it can’t be wished away Computational and mathematical organization theory Comput Math Organ Theory 18(1):5–21 Edmonds B, Hernández C, Trotzsh K (2007) Social simulation: technologies, advances and new discoveries ISBN: 9781599045221 Elsenbroich C (2012) Explanation in agent-based modelling: functions, causality or mechanisms? J Artif Soc Soc Simul 15(3):1 Epstein JM, Axtell R (1996) Growing artificial societies: social science from the bottom up Brookings Institution Press, Washington, DC Forrester JW (1961) Industrial dynamics Pegasus Communications, Waltham Freud S (1921) Group psychology and the analysis of the ego Hogarth Press Galam S, Wonczak S (2000) Dictatorship from majority rule voting Euro Phys J B 18(1):183–186 Gasull A, Kooij RE, Torregrosa J (1997) Limit cycles in the Holling-Tanner model 41 ISBN/ISSN ISSN 0214-1493 Goldberg A, Robson D (1989) Smalltalk 80: the language Addison-Wesley Professional ISBN: 0-201-13688-0 Gordon G (1975) The application of GPSS to discrete system simulation Prentice-Hall, Englewood Cliffs Gotts NM, Polhill JG, Law ANR (2003) Agent-based simulation in the study of social dilemmas Artif Intell Rev 9(1):3–92 Gras R, Devaurs D, Wozniak A, Aspinall A (2009) An individual-based evolving predator-prey ecosystem simulation using a fuzzy cognitive map as the behavior model Artif Life 15(4):423– 463 https://doi.org/10.1162/artl.2009.Gras.012 References 145 Grigg DB (1977) E. G Ravenstein and the laws of migration J Hist Geogr 3(1):41–54 Hamilton WD (1971) Geometry for the selfish herd J Theor Biol 31(2):295–311 https://doi org/10.1016/0022-5193(71)90189-5 Harris JR, Todaro MP (1970) Migration, unemployment and development: a two-sector analysis Am Econ Rev 60(1):126–142 Holcombe M, Coakley S, Kiran M (2013) Large-scale modelling of economic systems Compl Syst 22(2):175–191 http://www.complex-systems.com/pdf/22-2-3.pdf Holland JH (1998) Emergence: from chaos to order Helix Books: Addison-Wesley Publishing Company Hoppensteadt F (2006) Predator-prey model Scholarpedia 1(10):1563 http://www.scholarpedia org/article/predator-prey_model Hughes HPN, Clegg CW, Robinson MA, Crowder RM (2012) Agent-based modelling and simulation: the potential contribution to organizational psychology J Occup Organ Psychol 85:487–502 Kelton D, Sadowski R, Sadowski D (2004) Simulation with ARENA. McGraw-Hill, New York Klein J (2002) Breve: a 3D environment for the simulation of decentralized systems and artificial life Conference paper: ICAL 2003 Proceedings of the eighth international conference on Artificial life, MIT Press, Cambridge, MA. ISBN/ISSN 0-262-69281-3 Krause U (2000) A discrete nonlinear and non-autonomous model of consensus formation In: Elaydi S, Ladas G, Popenda J, Rakowski (eds) Communications in difference equations Gordon and Breach, Amsterdam Latane B, Nowak A (1997) Self-organizing social systems: necessary and sufficient conditions for the emergence of clustering, consolidation and continuing diversity In: Barnett FJ, Boster FJ (eds) Progress in communication sciences v.13 Ablex Publishing Corporation ISBN-13: 978-1567502770 Lee ES (1966) A theory of migration Demography 3(1):47–57 Leslie PH, Gower JC (1958) The properties of a stochastic model for two competing species Biometrica 45:316–330 Long JE (2002) Systems analysis: a tool to understand and predict terrorist activities Internet communication Vitech Corporation http://www.umsl.edu/~sauterv/analysis/62S-Long-INTEL.pdf Lotka AJ (1910) Contribution to the theory of periodic reaction J Phys Chem 14(3):271–274 Lotka AJ (1925) Elements of physical biology Williams & Wilkins Co, Baltimore Luke S, Cioffi-Revilla C, Panait L, Sullivan K (2005) MASON: a multiagent simulation environment Simulation 81(7):517–527 Lustick S (2000) Agent-based modeling of collective identity J Artif Soc Soc Simul 3(1) http:// jasss.soc.surrey.ac.uk/3/1/1.html Macy MW, Willer R (2002) From factors to actors: computational sociology and agent-based Modeling Annu Rev Sociol 28(1):143–166 Mangioni SE (2012) A mechanism for pattern formation in dynamic populations by the effect of gregarious instinct Phys A Stat Mech Appl 391:113–124 McDougall W (1926) The gregarious instinct In: Introduction to social psychology (revised edition) series: chapter 12 John W Luce & Co Michael JN, Nicholson T, Collier JR, Vos JR (2006) Experiences creating three implementations of the repast agent modeling toolkit ACM Trans Model Comput Simul 16(1):1–25 https://doi org/10.1145/1122012.1122013 Moss de Oliveira S, Stauffer D (1999) Evolution, money, war and computers – non- traditional applications of computational statistical physics Teubner, Stuttgart-Leipzig Nowak AM (2006) Evolutionary dynamics Harvard University Press, Cambridge, MA. ISBN: 9780674023383 O’Keefe RM (1986) The three-phase approach: a comment on strategy-related characteristics of discrete event languages and models Simulation 47(5):208–210 Parker MT (2001) What is ascape and why should you care? J Artif Soc Soc Simul http://jasss soc.surrey.ac.uk/4/1/5.html Pedgen CD, Sturrok DT (2010) Introduction to Simio Conference paper: proceedings of the 2010 Winter, PA, USA 146 References Pedgen CD, Shannon RF, Sadowski RP (1995) Introduction to simulation using SIMAN. McGraw- Hill, New York Perelson AS (1988) Toward a realistic model of immune system In: Theoretical immunology part II. Addison-Wesley Pontryagin LS, Boltyanskii VG, Gamkrelidze RV, Mishchenko EF (1962) The mathematical theory of optimal processes Interscience, New York ISBN: 2-88124-077-1 Powers DMW (1998) Applications and explanations of Zipf’s law Conference paper: ‘98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning, Sydney, Australia, ISBN/ISSN 0-7258-0634-6 Raczynski S (1980) Simulation of interaction between certain hierarchical structures Conference paper: Simulation of Large Systems, Universitat Bielefeld, Bielefeld Raczynski S (1988) Process hierarchy and inheritance in PASION. Simulation 50(6):249–251 Raczynski S (1989) Simulating our immune system Conference paper: simulation on microcomputers The Society for Computer Simulation Int ISBN/ISSN 0-911801-43-X Raczynski S (2000) Alternative mathematical tools for modeling and simulation: metric space of models, uncertainty, differential inclusions and semi-discrete events Conference paper: European Simulation Symposium ESS2000, Hamburg, Hamburg, Germany Raczynski S (2002) Differential inclusion solver Conference paper: International Conference on Grand Challenges for Modeling and Simulation, SCS, San Antonio Raczynski S (2003) Continuous simulation In: in encyclopedia of information systems Academic Press, Elsevier Publications, New York Raczynski S (2004) Simulation of the dynamic interactions between terror and anti-terror organizational structures J Artif Soc Soc Simul 7(2) ISBN/ISSN 1460-7425 Raczynski S (2006a) In: Bargiela A (ed) Modeling and simulation: computer science of illusion Wiley, Chichester Raczynski S (2006b) A self-destruction game J Nonlinear Dyn Psychol Life Sci 10(4):471–483 ISBN/ISSN 1090-0578 Raczynski S (2014) Simulating self-organization and interference between certain hierarchical structures Nonlinear Dyn Psychol Life Sci Hum Sci Press 18(4):419–434 ISBN/ISSN 1090-0578 Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review Simulation 82(9):609–623 https://doi.org/10.1177/0037549706073695 Reluga TC, Viscido S (2005a) Simulated evolution of selfish herd behavior J Theor Biol Elsevier 234(2):213–225 https://doi.org/10.1016/j.jtbi.2004.11.035 Reluga TC, Viscido S (2005b) Agent-based simulation: from modeling methodologies to real- world applications Rodrigue JP, Comtois C, Slack B (2009) The geography of transport systems Routledge, New York ISBN: 978-0-415-48324-7 Saam NJ, Harrer A (1999) Simulating norms, social inequality, and functional change in artificial societies J Artif Soc Soc Simul 2(1):1–2 Sambrook RC (2008) Spatial behavior analysis at the global level using fractal geometry Nonlinear Dyn Psychol Life Sci 12(1):3–13 Sibertin-Blanc C, Roggero P, Adreit F et al (2013) SocLab: a framework for the modeling, simulation and analysis of power in social organizations J Artif Soc Soc Simul 16(4):8 Silverman E, Bijak J, Hilton J et al (2013) When demography met social simulation: a tale of two modelling approaches J Artif Soc Soc Simul 16(4):9 Smith R (2001) Modeling and simulation adds insight on terrorism, signal magazine Signal Magazine, Armed forces Communications and Electronics Association Smith R (2002) Counter terrorism simulation: a new breed of federation Conference paper: Simulation Interoperability Workshop, Orlando, FL Staller A, Petta P (2001) Introducing emotions into the computational study of social norms: a first evaluation J Artif Soc Soc Simul 4(1):1–2 References 147 Stark O, Levhari D (1982) On migration and risk in LDCs Econ Dev Cult Chang 31(1):191–196 Stocker R, Cornforth D, Bossemaier RJ (2002) Network structures and agreement in social network simulations J Artif Soc Soc Simul 5(4):1–3 SWARM Development Group (2001) Swarm simulation system Electronic citation Electron Citation 8(1–10) http://digitalcommons.usu.edu/nrei/vol8/iss1/2 Takeuchi Y (1996) Global dynamical properties of Lotka-Volterra systems World Scientific, Singapore Tako AA, Robinson S (2007) Comparing the use of discrete-event simulation and system dynamics models Conference paper: 2007 Winter Simulation Conference, Washington, DC Tanner JT (1975) The stability and intrinsic growth rates of prey and predator populations Ecology 56(1):856–867 https://doi.org/10.2307/1936296 Tanuma H, Deguchi H, Shimizu T (2005) Agent-based simulation: from modeling methodologies to real-world applications, vol Springer, Tokyo Tanuma H, Deguchi H, Shimizu T (2006) SOARS: Spot Oriented Agent Role Simulator – design and implementation In: Agent-based simulation: from modeling methodologies to real-world applications Springer, Tokyo, ISBN 9784431269250 Tatai G, Gulyas L, Laufer L, Ivanyi M (2005) Artificial agents helping to stock up on knowledge Conference paper: 4th International Central and Eastern European Conference on MultiAgent System, Budapest, Hungary, ISBN:3-540-29046-X 978-3-540-29046-9 https://doi org/10.1007/11559221_3 Trotter W (1919) Instincts of the herd in peace and war McMillan, New York Volterra V (1926) Variazioni e fluttuazioni del numero d’individui in specie animali conviventi Volterra V (1931) Variations and fluctuations of the number of individuals in animal species living together McGraw-Hill, New York Wadycki WJ (1975) Stouffer’s model of migration: a comparison of interstate and metropolitan flows Demography 12(1):121–128 Williams BA (1933) Gregariousness: a critical examination of the concept of the gregarious instinct Australasian J Psychol Philos 11(1):50–68 Willis CE (1992) The effect of sex role stereotype, victim and defendant race, and prior relationship on rape culpability attributions Sex Roles 26(5–6):213–226, ISSN: 0360-0025 Younger SM (2003) Discrete agent simulations of the effect of simple social structures on the benefits of resource J Artif Soc Soc Simul 6(3) Zeigler BP (1976) Theory of modeling and simulation Wiley-Interscience, New York Zhang Z, Yang H, Liu J (2012) Stability and Hopf bifurcation in a modified Holling- Tanner predator-prey system with multiple delays Abstr Appl Anal, Euclid, https://doi org/10.1155/2012/236484 Index A Agent based model (ABM), 1, 70 Animation, Arena, Ascape, Attributes of agents, 37 B Bandwagon, 48 BC model, 19 BLUESSS, Bond graph, 7, 10 Breve, C Changing organization, 58 Competition, 127, 128, 131 Cormas, Corruption, 47, 55, 59 Corruption level, 24 D Decay of organization, 47 Differential inclusion, 110, 111 Discrete event specification (DEVS), E Epidemics, 122 Evolution, 74, 114, 116, 124, 125 F Fear, 116 G General purpose simulation system (GPSS), Gravity migration model, 84 Gregarious, 47, 67 Gregarious instinct, 67 H Herd instinct, 47, 61, 67 Hierarchical structure, 34 I Immune system model, 12 Infiltration, 36 Interactions between structures, 36, 37 L Lee migration model, 84 Lotka-Volterra (L-V), 107 Lust for power, 22 M Macro behavior, 70, 85 Macro model, Manufacturing model, © Springer Nature Singapore Pte Ltd 2020 S Raczynski, Interacting Complexities of Herds and Social Organizations, Evolutionary Economics and Social Complexity Science 19, https://doi.org/10.1007/978-981-13-9337-2 149 150 MASON, Micro model, 1, 70 Migration, 83 Migration patterns, 89 Model time, O Object behavior modeling (OBM), Orbitally stable, 27 Oscillating organization, 27 P PASION, PM corruption field, 22 Political map (PM), 21, 52 Prey-predator, 107 PSM++, Q Queuing model, Queuing model generator (QMG), Index SIMAN, SIMIO, Simula, SOARS, Soft system, 110 Space of ideas, 52 Stark migration model, 84 Stouffer theory, 84 Structure, 37 SWARM, System dynamics, 133 T Terrorism, 31 Terrorism model, 34 Terrorist, 35 Terrorist structure, 34 Threat, 76 Time and event management (TEM), 3, Todaro model, 84 U Uncertailnty, 110, 111 R Ravenstein migration model, 84 Reachable set, 110 Real time, S Self-destruction, 97–105 Signal flow, 10 V Validity of models, 33 Variance, 10 Z Zipf’s law, 84 ... creation of social structures in the process of food and material storage Some more general concepts of computational sociology and agent- based modeling can be found in the article of Macy and Willer... at http://www.springer.com/series/11930 Stanislaw Raczynski Interacting Complexities of Herds and Social Organizations Agent Based Modeling Stanislaw Raczynski Facultad de Ingeniería Universidad... some agent based modeling tools The other chapters contain examples of applications to artificial societies and competing populations of individuals and the growth, interactions, and decay of organizations