DOCSLIB.ORG

A Primer on Complexity: an Introduction to Computational Social Science

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Primer on Complexity: an Introduction to Computational Social Science A Primer on Complexity: An Introduction to Computational Social Science Shu-Heng Chen Department of Economics National Chengchi University Taipei, Taiwan 116 [email protected] 1 Course Objectives This course is to enable the students of social sciences to have some initial experiences of computational modeling of social phenomena or social processes.In this regard, it severs as the beginning course for preparing students of social sciences to have computational thinkingof social phenomena.On the other hand, the course is also to serve as a beginning course for the students of computer science or students with a strong taste for program- ming andmodeling who are, however, very curious of social phenomena, and want to see whether they can develop their talents in the axis of social sciences. 2 Course Description National Chengchi University is internationally well-known for its social sciences, but what are the natures of social sciences, and, to what extent, do they differ from sciences? Are these differences fundamentally or just superficially? The current social sciences are divided into economics, business, management, political sciences, sociology, psychology, public administration, anthropology, ethnology, religion, geography, law, international relations, etc. Is this division necessary, logical? When a social phenomenon is presenting itself to us, such as populism, polarization, segregation, racism, exploitation, injustice, global warming, income inequality, social exclusiveness or inclusiveness, green energy promotion, anti-gouging legislation, riots, tuition freeze, mass media regulation, finan- cial crisis, nationalism, globalization, will it identify itself: to which sister discipline it belongs? Economics? Psychology? Ethnology? If the purpose of social sciences is to enhance our understanding (harness) of social phenomena, and hence to enable us to develop a good conversation quality among cit- izens of the society from which the social phenomena emerge, then we probably need to promote good conversations among social scientists first. This is the so-called social science in an interdisciplinary context. Without sufficient conversations among social sci- entists first, we are afraid that the students of the current social sciences with so many disciplines may learn a lot, but in a rather fragmental way. 1 Therefore, while entering into different gates of the social science castle, maybe it is useful to have a more generic, comprehensive, jargon-free pursuit of what social science exactly is. What are the rudimentary elements? What are their generic properties? This fundamental pursuit is the focusing subject of this course, which actually makes social science a coherent family. The course provides two answers to this fundamental pursuit.First, as the title of the course suggests, social sciences deal with social phenomena with different degrees of complexity. Some examples from the magnum opus of Thomas Schelling (1921-2016) “Micro Motives and Macro Behavior” (Schelling, 1978) are sufficient enough to illustrate whatwe mean by complexity in social phenomena. In Week 5, we will review his fa- mous work on the segregation phenomenon observed in many metropolitan areas, such as New York, Chicago, and Los Angeles. Why do people of different races tend to be lumped together in residence? Is it because they are not tolerant enough for different eth- nic groups? If individually they each are quite tolerant of the unlike people, would that be surprising if collectively they don’t seem so? This segregation example shows us one lesson: knowledge of individuals may help us little in predicting what would happen ’as a whole’, and this is what makes social science intriguing, fascinating, and complex. However, just in a verbal way, the word complexity does not automatically unveil all deep functioning mechanisms underlying complex social phenomena.To know the “chemistry” which can add up all simple things in a complex form, we need to simulate these social phenomena. Without going deep into the social process and successfully sim- ulating the observed phenomena, our eyes and minds are frequently under great expo- sure to being deceived by what may seem obvious. Nevertheless, as we shall experience in this class (say the Game of Life, Week 2), paper and pencils are far less than enough for simulating social phenomena, even though what we need to consider is just a small-scale primitive society. Hence, here comes our second response (answer) to the fundamental pursuit, i.e., com- putational social science, the subtitle of the class. The idea is to take full advantage of the great advancement in high-performance computing to tackle complex social phenomena. Having said that, this class does not intend to challenge or scare away those students with programming phobia, most likely, students from social science departments. By no means we will teach anything in programming seriously. In fact, the current devel- opment of computational social science already makes it much more approachable with so many easy-to-use software (languages), and NetLogo is one of them (Railsback and Grimm, 2011; Hamill and Gilbert, 2015; Wilensky and Rand, 2015). In this class, we shall use NetLogo to make students have a quick grasp of the three rudimental elements of all social phenomena, namely, agents, also called actors, decision- makers (their personal traits and behavioral rules), social embeddedness (institutions, cultures, laws, history, spatial surroundings), and social interactions. As we shall see in many examples, aided by NetLogo, these three rudimental elements not only are able to simulate many interesting social phenomena but can also overarch various sister disci- plines of the social science family. At the end of the class, we shall leave all students a chance to answer the questions which we begin with. First, is it really a fundamental difference between science and social science? Second, is it really necessary to have so many different treatments for the same social phenomena with so many different disciplines, from A to Z; would it be better if we can have a coher- 2 ent or united framework so that we would be only interestedin things by its relevancy not by its disciplines. Third, can we say something about the future of social science, in particular, in light of shaping a good society? 3 Class Schedule General Specification: 1. The student is expected to spend 9 hours per week on this course, which means a 6-hour preparation and review work plus 3-hour class attendance. 2. The assignment (the reading and the computer simulation exercises) will be given at the end of each ppt of the lecture. 3. The student is required to reflect upon the lecture received the week and propose questions, comments, or observations before the lecture of the next week. This “weekly report” needs to be submitted to the web as indicated in the class. The late submission will not be accepted. Weekly Progress: 1. Week One (Lectured on Feb 24, 2021): Background, History, and All Warm-Ups: (a) Let us begin the story with a genius, John von Neumann (1903-1957) (b) When Social Science become computational and Computer Science become biological? (c) A fascinating history of computational social science 2. Week Two (Lectured on March 3, 2021): John Conway and His Game of Life: CSS coming to the 1970s (a) How simple things get so complex and hard to predict? (b) Can Life extend without limit? Can you win the award? (c) It is all in NetLogo 3. Week Three (Lectured on March 10, 2021): Stephen Wolfram and New Kind of Science: CSS coming to the 1980s (a) Butterfly Effect: Sensitivity to small changes (b) Small is Big: There is nothing unimportant. (c) Identify new types of social phenomena: Systematic way doing social science (d) A first simulation for interesting social dynamics: On the edge of chaos (e) NetLogo, Computer Science, Cellular Automata 4. Week Four (Lectured on March 17, 2021): Sustainable Development and Ecological Balance 3 (a) Uri Wilensky and his perception of science education: Low threshold and high ceiling (b) Wolves and sheep in meadows (c) Alfred Lotka (1880-1949) and Vito Volterra (1860-1940) (d) Predatory-and-Prey Dynamics: Lotka-Volterra Equation (e) NetLogo Models Library: Sample Models/Biology, Wolf Sheep Predation (Wilen- sky and Rand, 2015, Chapter 4) 5. Week Five (Lectured on March 24, 2021): Segregation in Metropolitan Areas (a) Thomas Schelling: The 2005 Nobel Laureate in Economics (b) Why people with different ethnic groups choose different residential concen- tration? (c) Thomas Schelling and his Segregation Models (d) NetLogo Models Library: Sample Models/Social Science, Segregation 6. Week Six (Lectured on March 31, 2021): Tutorial on Netlogo Programming (lectured by Dr. Tina Yu). Please bring your own computer. 7. Week Seven (Lectured on April 7, 2021): Public Health and Epidemiology (a) AIDS, SARS, and Bird Flu (b) NetLogo Models Library: Sample Models/Social Science, AIDS 8. Week Eight (Lectured on April 14, 2021): Efficiency and Equity: The El Farol Bar Problem (a) Brian Arthur and the El Farol Bar (b) Congestion and idle capacity: Too much and too little (c) Social Exclusion (d) NetLogo Models Library: Sample Models/Social Science, El Farol 9. Week Nine(April 21, 2021) Midterm Exam 10. Week Ten (Lectured on April 28, 2021): Traffic in the High Way (a) Drivers’ behavior and Traffic (b) NetLogo Models Library: Sample Models/Social Science, Traffic Basic, Grid, 2 lines 11. Week Eleven (Lectured on May 5, 2021): Gossip and Social Networks 4 (a) Gossip: Everybody like it so long as it is not about you (b) Social network: Your Facebook, Skype,... (c) How fast gossip can go: Significance of social networks (d) NetLogo Models Library: Sample Models/Social Science, Rumor Mill 12. Week Twelve (Lectured on May 12, 2021): Time and Hero: Who Made Whom? (a) Reynold Boyd and His Flocking Project (b) Swarms and Their Intelligence (c) Who made a hero? Nobody, it is all self-organizing! (d) NetLogo Models Library: Sample Models/Biology, Flocking 13.
Load more

Recommended publications
  • Varieties of Emergence
    See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/228792799 Varieties of emergence Article · January 2002 CITATIONS READS 37 94 1 author: Nigel Gilbert University of Surrey 364 PUBLICATIONS 9,427 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Consumer Behavior View project Agent-based Macroeconomic Models: An Anatomical Review View project All content following this page was uploaded by Nigel Gilbert on 19 May 2014. The user has requested enhancement of the downloaded file. 1 VARIETIES OF EMERGENCE N. GILBERT, University of Surrey, UK* ABSTRACT** The simulation of social agents has grown to be an innovative and powerful research methodology. The challenge is to develop models that are computationally precise, yet are linked closely to and are illuminating about social and behavioral theory. The social element of social simulation models derives partly from their ability to exhibit emergent features. In this paper, we illustrate the varieties of emergence by developing Schelling’s model of residential segregation (using it as a case study), considering what might be needed to take account of the effects of residential segregation on residents and others; the social recognition of spatially segregated zones; and the construction of categories of ethnicity. We conclude that while the existence of emergent phenomena is a necessary condition for models of social agents, this poses a methodological problem for those using simulation to investigate social phenomena. INTRODUCTION Emergence is an essential characteristic of social simulation. Indeed, without emergence, it might be argued that a simulation is not a social simulation.
    [Show full text]
  • Self-Regulation Via Neural Simulation
    Self-regulation via neural simulation Michael Gileada,b,1, Chelsea Boccagnoa, Melanie Silvermana, Ran R. Hassinc, Jochen Webera, and Kevin N. Ochsnera,1 aDepartment of Psychology, Columbia University, New York, NY 10027; bDepartment of Psychology, Ben-Gurion University of the Negev, Be’er Sheva 8410501, Israel; and cDepartment of Psychology, The Hebrew University of Jerusalem, Jerusalem 9190501, Israel Edited by Marcia K. Johnson, Yale University, New Haven, CT, and approved July 11, 2016 (received for review January 24, 2016) Can taking the perspective of other people modify our own affective Although no prior work has addressed these questions, per se, responses to stimuli? To address this question, we examined the the literatures on emotion regulation (1, 5–11) and perspective- neurobiological mechanisms supporting the ability to take another taking (12–18) can be integrated to generate testable hypotheses. person’s perspective and thereby emotionally experience the world On one hand, research on emotion regulation has shown that as they would. We measured participants’ neural activity as they activity in lateral prefrontal cortex (i.e., dorsolateral prefrontal attempted to predict the emotional responses of two individuals that cortex and ventrolateral prefrontal cortex) and middle medial differed in terms of their proneness to experience negative affect. prefrontal cortex (i.e., presupplementary motor area, anterior Results showed that behavioral and neural signatures of negative ventral midcingulate cortex, and anterior dorsal midcingulate affect (amygdala activity and a distributed multivoxel pattern reflect- cortex) (19) supports the use of cognitive strategies to modulate ing affective negativity) simulated the presumed affective state of activity in (largely) subcortical systems for triggering affective the target person.
    [Show full text]
  • Theory Development with Agent-Based Models 1
    Running head: THEORY DEVELOPMENT WITH AGENT-BASED MODELS 1 Theory Development with Agent-Based Models Paul E. Smaldino Johns Hopkins University Jimmy Calanchini University of California, Davis Cynthia L. Pickett University of California, Davis In press at Organizational Psychology Review Running head: THEORY DEVELOPMENT WITH AGENT-BASED MODELS 2 Abstract Many social phenomena do not result solely from intentional actions by isolated individuals, but rather emerge as the result of repeated interactions among multiple individuals over time. However, such phenomena are often poorly captured by traditional empirical techniques. Moreover, complex adaptive systems are insufficiently described by verbal models. In this paper, we discuss how organizational psychologists and group dynamics researchers may benefit from the adoption of formal modeling, particularly agent-based modeling, for developing and testing richer theories. Agent-based modeling is well-suited to capture multi-level dynamic processes and offers superior precision to verbal models. As an example, we present a model on social identity dynamics used to test the predictions of Brewer’s (1991) optimal distinctiveness theory, and discuss how the model extends the theory and produces novel research questions. We close with a general discussion on theory development using agent-based models. Keywords: group dynamics, agent-based modeling, formal models, optimal distinctiveness, social identity Running head: THEORY DEVELOPMENT WITH AGENT-BASED MODELS 3 Introduction "A mathematical theory may be regarded as a kind of scaffolding within which a reasonably secure theory expressible in words may be built up. ... Without such a scaffolding verbal arguments are insecure." – J.B.S. Haldane (1964) There are many challenges that face group dynamics researchers.
    [Show full text]
  • Modeling Memes: a Memetic View of Affordance Learning
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Spring 2011 Modeling Memes: A Memetic View of Affordance Learning Benjamin D. Nye University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Artificial Intelligence and Robotics Commons, Cognition and Perception Commons, Other Ecology and Evolutionary Biology Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons, Social Psychology Commons, and the Statistical Models Commons Recommended Citation Nye, Benjamin D., "Modeling Memes: A Memetic View of Affordance Learning" (2011). Publicly Accessible Penn Dissertations. 336. https://repository.upenn.edu/edissertations/336 With all thanks to my esteemed committee, Dr. Silverman, Dr. Smith, Dr. Carley, and Dr. Bordogna. Also, great thanks to the University of Pennsylvania for all the opportunities to perform research at such a revered institution. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/336 For more information, please contact [email protected]. Modeling Memes: A Memetic View of Affordance Learning Abstract This research employed systems social science inquiry to build a synthesis model that would be useful for modeling meme evolution. First, a formal definition of memes was proposed that balanced both ontological adequacy and empirical observability. Based on this definition, a systems model for meme evolution was synthesized from Shannon Information Theory and elements of Bandura's Social Cognitive Learning Theory. Research in perception, social psychology, learning, and communication were incorporated to explain the cognitive and environmental processes guiding meme evolution. By extending the PMFServ cognitive architecture, socio-cognitive agents were created who could simulate social learning of Gibson affordances.
    [Show full text]
  • Manifesto of Computational Social Science R
    Manifesto of computational social science R. Conte, N. Gilbert, G. Bonelli, C. Cioffi-Revilla, G. Deffuant, J. Kertesz, V. Loreto, S. Moat, J.P. Nadal, A. Sanchez, et al. To cite this version: R. Conte, N. Gilbert, G. Bonelli, C. Cioffi-Revilla, G. Deffuant, et al.. Manifesto of computational social science. The European Physical Journal. Special Topics, EDP Sciences, 2012, 214, p. 325 - p. 346. 10.1140/epjst/e2012-01697-8. hal-01072485 HAL Id: hal-01072485 https://hal.archives-ouvertes.fr/hal-01072485 Submitted on 8 Oct 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Eur. Phys. J. Special Topics 214, 325–346 (2012) © The Author(s) 2012. This article is published THE EUROPEAN with open access at Springerlink.com PHYSICAL JOURNAL DOI: 10.1140/epjst/e2012-01697-8 SPECIAL TOPICS Regular Article Manifesto of computational social science R. Conte1,a, N. Gilbert2, G. Bonelli1, C. Cioffi-Revilla3,G.Deffuant4,J.Kertesz5, V. Loreto6,S.Moat7, J.-P. Nadal8, A. Sanchez9,A.Nowak10, A. Flache11, M. San Miguel12, and D. Helbing13 1 ISTC-CNR, Italy 2 CRESS, University
    [Show full text]
  • Mechanistic Models in Computational Social Science
    Mechanistic Models in Computational Social Science Petter Holme1*, Fredrik Liljeros2 1 Department of Energy Science, Sungkyunkwan University, 440-746 Suwon, Korea 2 Department of Sociology, Stockholm University, 10961 Stockholm, Sweden * Correspondence: Petter Holme, [email protected] Abstract Quantitative social science is not only about regression analysis or, in general, data in- ference. Computer simulations of social mechanisms have an over 60 years long his- tory. Tey have been used for many diferent purposes—to test scenarios, to test the consistency of descriptive theories (proof-of-concept models), to explore emergent phe- nomena, for forecasting, etc. In this essay, we sketch these historical developments, the role of mechanistic models in the social sciences and the infuences from the natural and formal sciences. We argue that mechanistic computational models form a natural common ground for social and natural sciences, and look forward to possible future information fow across the social-natural divide. Background In mainstream empirical social science, a result of a study often consists of two conclu- sions. First, that there is a statistically signifcant correlation between a variable de- scribing a social phenomenon and a variable thought to explain it. Second, that the correlations with other, more basic, or trivial, variables (called control, or confounding, variables) are weaker. Tere has been a trend in recent years to criticize this approach for putting too little emphasis on the mechanisms behind the correlations [Woodward 2014, Salmon 1998, Hedström Ylikoski 2010]. It is often argued that regression analysis (and the linear, additive models they assume) cannot serve as causal explanations of an open system such as usually studied in social science.
    [Show full text]
  • A Common Protocol for Agent-Based Social Simulation Abstract 1
    A Common Protocol for Agent-Based Social Simulation Roberto Leombruni, Matteo Richiardi, LABORatorio Revelli Centre for Employment Studies, via Real Collegio 30, 10024 Moncalieri, Torino, Italy. Email: [email protected] Nicole J. Saam, Institut für Soziologie Ludwig-Maximilians-Universität München, Konradstr. 6 D-80801 München, Germany. Email: [email protected] Michele Sonnessa, Department of Computer Science, University of Torino, Corso Svizzera 185, Torino, Italy. Email: [email protected] Abstract Traditional (i.e. analytical) modelling practices in the social sciences rely on a very well established, although implicit, methodological protocol, both with respect to the way models are presented and to the kinds of analysis that are performed. Unfortunately, computer-simulated models often lack such a reference to an accepted methodological standard. This is one of the main reasons for the scepticism among mainstream social scientists that results in low acceptance of papers with agent-based methodology in the top journals. We identify some methodological pitfalls that, according to us, are common in papers employing agent-based simulations, and propose appropriate solutions. We discuss each issue with reference to a general characterization of dynamic micro models, which encompasses both analytical and simulation models. In the way, we also clarify some confusing terminology. We then propose a three-stage process that could lead to the establishment of methodological standards in social and economic
    [Show full text]
  • Simulating the Spatial Diffusion of Memes on Social Media Networks
    International Journal of Geographical Information Science ISSN: 1365-8816 (Print) 1362-3087 (Online) Journal homepage: https://www.tandfonline.com/loi/tgis20 Simulating the spatial diffusion of memes on social media networks Lanxue Dang, Zhuo Chen, Jay Lee, Ming-Hsiang Tsou & Xinyue Ye To cite this article: Lanxue Dang, Zhuo Chen, Jay Lee, Ming-Hsiang Tsou & Xinyue Ye (2019) Simulating the spatial diffusion of memes on social media networks, International Journal of Geographical Information Science, 33:8, 1545-1568, DOI: 10.1080/13658816.2019.1591414 To link to this article: https://doi.org/10.1080/13658816.2019.1591414 Published online: 23 May 2019. Submit your article to this journal Article views: 556 View related articles View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tgis20 INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2019, VOL. 33, NO. 8, 1545–1568 https://doi.org/10.1080/13658816.2019.1591414 RESEARCH ARTICLE Simulating the spatial diffusion of memes on social media networks Lanxue Danga, Zhuo Chenb, Jay Leeb,c, Ming-Hsiang Tsoud and Xinyue Yee aHenan Key Laboratory of Big Data Analysis and Processing, School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China; bDepartment of Geography, Kent State University, Kent, OH, USA; cCollege of Environment and Planning, Henan University, Kaifeng, Henan, China; dDepartment of Geography, San Diego State University, San Diego, CA, USA; eUrban Informatics & Spatial Computing Lab, Department of Informatics, New Jersey Institute of Technology, Newark, NJ, USA ABSTRACT ARTICLE HISTORY This article reports the findings from simulating the spatial diffu- Received 5 September 2018 sion processes of memes over social media networks by using the Accepted 4 March 2019 approach of agent-based modeling.
    [Show full text]
  • Agent-Based Computational Economics Documentation Release 0.9.3B0
    Agent-Based Computational Economics Documentation Release 0.9.3b0 Davoud Taghawi-Nejad Jul 24, 2021 Contents 1 Introduction 3 1.1 Design..................................................3 1.2 Download and Installation........................................7 1.3 Interactive jupyter notebook Tutorial..................................9 1.4 Walk through...............................................9 1.5 Tutorial for Plant Modeling....................................... 16 1.6 Examples................................................. 20 1.7 unit testing................................................ 24 2 Simulation Programming 25 2.1 The simulation in start.py........................................ 25 2.2 Agents.................................................. 28 2.3 Groups.................................................. 29 2.4 Physical goods and services....................................... 31 2.5 Trade................................................... 32 2.6 Messaging................................................ 32 2.7 Firm and production........................................... 32 2.8 Household and consumption....................................... 35 2.9 Observing agents and logging...................................... 36 2.10 Retrieval of the simulation results.................................... 37 2.11 NotEnoughGoods Exception....................................... 38 3 Advanced 39 3.1 Quote................................................... 39 3.2 Spatial and Netlogo like Models..................................... 39
    [Show full text]
  • Social Emergence: Distinguishing Reflexive and Non-Reflexive Modes
    Social Emergence: Distinguishing Reflexive and Non-reflexive Modes Christopher Goldspink Robert Kay Centre for Research in Social Simulation Head of Strategic Innovation, Department of Sociology Westpac Banking Corporation, University of Surrey. Guildford, GU1 7XH, UK Level 8, Westpac Place, 275 Kent Street, Sydney NSW 2000 [email protected] [email protected] Abstract within the context of human social systems by focusing on Emergence has a long and controversial history. In this paper processes of ‘normative’ self-organization. The aim is to we briefly review the primary strands of the debate, paying contribute both to the conceptualization of emergence as attention to its use in the fields of philosophy of science and well as to how social emergence may be meaningfully mind, social science and systems theory including the theory modeled. of complex systems. We argue that it is important to recognize We argue that the ambiguity, opaqueness and lack of why emergence in social systems is fundamentally different specification of the concept of emergence currently present a from other natural systems. The key characteristics of significant barrier to its application to the study of social reflexivity are discussed and a distinction between two systems. Furthermore we argue that social systems classes of emergence proposed. Non-reflexive emergence: represent a specific class of system, distinct from other where the agents in the system under study are not self-aware, and Reflexive emergence: where the agents in the system under natural
    [Show full text]
  • Agent-Based Computational Models and Generative Social Science
    Agent-Based Computational Models And Generative Social Science JOSHUA M. EPSTEIN Economic Studies, Brookings Institution, Washington, D.C., 20036, USA External Faculty, Santa Fe Institute, Santa Fe, NM, 87501, USA e-mail: [email protected] Received April 6, 1999; accepted May 18, 1999 This article argues that the agent-based computational model permits a distinctive approach to social science for which the term “generative” is suitable. In defending this terminology, features distinguishing the approach from both “inductive” and “deductive” science are given. Then, the following specific contributions to social science are discussed: The agent-based computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agent-based modeling pro- vides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agent-based (“bottom up”) models. The agent-based approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agent-based modeling offers powerful new forms of hybrid theo- retical-computational work; these are particularly relevant to the study of non-equilibrium systems. The agent- based approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation.
    [Show full text]
  • A Conceptual Framework for the Modelling and Simulation of Social Systems
    DYNA ISSN: 0012-7353 ISSN: 2346-2183 Universidad Nacional de Colombia A conceptual framework for the modelling and simulation of social systems Awad, Gabriel; Alvarez, Hernán A conceptual framework for the modelling and simulation of social systems DYNA, vol. 87, no. 212, 2020 Universidad Nacional de Colombia Available in: http://www.redalyc.org/articulo.oa?id=49663642023 DOI: 10.15446/dyna.v87n212.83266 PDF generated from XML JATS4R by Redalyc Project academic non-profit, developed under the open access initiative Artículos A conceptual framework for the modelling and simulation of social systems Un marco conceptual para el modelado y la simulación de sistemas sociales Gabriel Awad a [email protected] Universidad Nacional de Colombia, Colombia Hernán Alvarez b [email protected] Universidad Nacional de Colombia, Colombia Abstract: is paper presents a conceptual framework for the modelling and simulation of properties, interactions and processes of social systems based on computational templates using discrete event system specification (DEVS) formalism and OMG DYNA, vol. 87, no. 212, 2020 Systems Modelling Language (SysML) diagrams. No antecedents of this combination Universidad Nacional de Colombia were found in the literature, and so this is one of the contributions of this paper. Received: 30 October 2019 Additionally, this article explains how the principles and rules of SysML can be applied Revised document received: 24 January to the analysis of social systems. An example of the proposed framework based on a basic 2020 Agent_Zero model is shown. e conceptual framework was built based on a critical Accepted: 10 February 2020 literature review, and included new additional elements to create a complete but simple DOI: 10.15446/dyna.v87n212.83266 conceptual framework.
    [Show full text]