1. 1 Complex Adaptive Systems and Agent-Based Simulation Approach: theoretical aspects Ing. Cristina Ponsiglione ponsigli@unina.itDII University of Naples Federico II Laboratorio di Simulazione ad Agenti Sistemi per la Gestione Aziendale 2013/2014 Research Methodologies: Agent-Based Systems and Social Simulation

2. 2 Summary Introduction to Agent-Based Simulation; Introduction to Agent-Based Simulation; Complexity and Complex-Adaptive Systems; Complexity and Complex-Adaptive Systems; The Generative Social Science and the Agent- Based Approach; The Generative Social Science and the Agent- Based Approach; Basic components of Agent-Based Models. Basic components of Agent-Based Models.

3. 3 Main References Holland J. H., (1995), Hidden order. How Adaptation Builds Complexity, Addison-Wesley Publishing Company. Holland J. H., (1995), Hidden order. How Adaptation Builds Complexity, Addison-Wesley Publishing Company. Epstein J., Axtell. R., (1996), Growing artificial societies: social science from the bottom up, Cambridge (MA), MIT Press. Epstein J., Axtell. R., (1996), Growing artificial societies: social science from the bottom up, Cambridge (MA), MIT Press. Terna P., Boero R., Morini M., Sonnessa M., (2006) Modelli per la complessità. La simulazione ad agenti in economia, Bologna, Il Mulino. Terna P., Boero R., Morini M., Sonnessa M., (2006) Modelli per la complessità. La simulazione ad agenti in economia, Bologna, Il Mulino. Harrison J.R, Lin Z., Carroll G. R., Carley K. M, (2007), Simulation Modeling in Organizational and Management Research, Academy of Management Review, Vol 32., N. 4, 1229-1245. Harrison J.R, Lin Z., Carroll G. R., Carley K. M, (2007), Simulation Modeling in Organizational and Management Research, Academy of Management Review, Vol 32., N. 4, 1229-1245.

4. 4 Introduction

5. 5 Models Typology Physical Models (i.e. reproduction of an object to be studied); Physical Models (i.e. reproduction of an object to be studied); Descriptive/Linguistic Models (i.e. reproduction of a phenomenon through natural language); Descriptive/Linguistic Models (i.e. reproduction of a phenomenon through natural language); Analytical Models ( i.e. models using differential equations ) Analytical Models ( i.e. models using differential equations ) Simulative Models (i.e. models for calculation, system dynamics models, agent-based models) Simulative Models (i.e. models for calculation, system dynamics models, agent-based models)

6. 6 Models Typology Physical Models (not usable in social sciences and in the presence of high complexity); Physical Models (not usable in social sciences and in the presence of high complexity); Descriptive/Linguistic Models (flexibility, not usable to calculate); Descriptive/Linguistic Models (flexibility, not usable to calculate); Analytical Models (possibility of calculation, need of simplification) Analytical Models (possibility of calculation, need of simplification) Simulative Models (flexibility and possibility of calculation) Simulative Models (flexibility and possibility of calculation)

7. 7 The Use of Simulation Simulation as a speculative complement to a descriptive model; Simulation as a speculative complement to a descriptive model; Simulation as a tool to calculate numerical solutions of a mathematical model (operations research, system dynamics); Simulation as a tool to calculate numerical solutions of a mathematical model (operations research, system dynamics); Simulation as a research approach in the presence of complex adaptive systems. Simulation as a research approach in the presence of complex adaptive systems.

8. 8 Complexity Is the property of a system in which the agents local actions and interactions produce aggregate behaviors different from the sum of individuals behaviors. ( Holldobrer and Wilson, 1997) The use of simulation in a descriptive or in a pure calculative way does not permit to consider a complex phenomenon in its parts and at all at the same moment.

9. 9 Complicated and Complex A complicated system is a system in which is simple to deduce the global behavior knowing the behavior of its parts. A complicated system is a system in which is simple to deduce the global behavior knowing the behavior of its parts. A complex system is a system in which the simple analysis of isolated parts does not produce the understanding of collective-aggregate behaviors. A complex system is a system in which the simple analysis of isolated parts does not produce the understanding of collective-aggregate behaviors.

10. 10 Agent-Based simulations Representation of behaviors of individuals populating the system through informatics routines and algorithms; Representation of behaviors of individuals populating the system through informatics routines and algorithms; Focus on descriptive aspects of reality (not equations); Focus on descriptive aspects of reality (not equations); Possibility of calculus offered by computers; Possibility of calculus offered by computers; Useful to conduct generative experiments in a virtual laboratory. Useful to conduct generative experiments in a virtual laboratory.

11. 11 Complex Adaptive Systems

12. 12 Linear and not-linear systems A linear system is a system in which the global behavior is the sum of isolated individuals-locals behaviors (start from the system, decompose the system in its parts and analyze them as isolated parts); A linear system is a system in which the global behavior is the sum of isolated individuals-locals behaviors (start from the system, decompose the system in its parts and analyze them as isolated parts); A non-linear system is a system in which the global behavior is not the sum of local-individual behaviors (start from the parts, analyze their interactions and then re-construct the global behavior). A non-linear system is a system in which the global behavior is not the sum of local-individual behaviors (start from the parts, analyze their interactions and then re-construct the global behavior).

13. 13 The Complexity Approach Six features of Economy that make difficult the use of mathematics and analytical models (Arthur, Durlauf, Lane, 1997- Santa Fe Institute) Dispersed interaction (autonomous and heterogeneous agents interact in a parallel way); Dispersed interaction (autonomous and heterogeneous agents interact in a parallel way); No-global controller (no global entity controls interactions, control is provided by local mechanisms of cooperation and competition); No-global controller (no global entity controls interactions, control is provided by local mechanisms of cooperation and competition); Cross-Cutting Hierarchical Organization (many levels of organization with many communications between levels-the parts of a level make the building block of another level); Cross-Cutting Hierarchical Organization (many levels of organization with many communications between levels-the parts of a level make the building block of another level); Continual Adaptation (agents continuously adapt) ; Continual Adaptation (agents continuously adapt) ; Perpetual Novelty (new markets, new technologies and so on…); Perpetual Novelty (new markets, new technologies and so on…); Out-of-Equilibrium Dynamics (the Economy operates far from any global equilibrium- improvements are always possible). Out-of-Equilibrium Dynamics (the Economy operates far from any global equilibrium- improvements are always possible).

14. 14 The Seven Basics (Holland, 1995) 1. Aggregation property (simplification of complex systems through categories that became building blocks for the model – emergence of complex large-scale behaviors from aggregate interactions of less complex agents); 2. Tagging mechanism (a kind of mechanism that permits the formation of aggregates-this mechanism allows agents to define specific patterns of interaction. For istance consider the pheromon acting as driving mechanism for ants in a colony); 3. Non-linearity property; 4. Flows property (flows of resources through connectors and nodes of networks – all CAS are networks of this kind); 5. Diversity property (heterogeneity of agents populating a CAS – not random or accidental, but depending of the evolution of the system); 6. Internal models mechanism (CAS anticipate thanks to the internal schemata of agents- an internal model is a mechanism to predict behaviors of agents); 7. Building blocks mechanism (is the mechanism by which is possible to decompose a complex system in reusable (through different combinations) parts – this mechanism serves to impose regularity to complex systems)

15. 15 Examples of CAS New York City (buyers, sellers, administrators, buildings, streets, bridges – No single constituent remains in place, but the city persists over time- It is a pattern in time); New York City (buyers, sellers, administrators, buildings, streets, bridges – No single constituent remains in place, but the city persists over time- It is a pattern in time); The human immune system (community of cells named antibodies that repel or destroy antigens – Antigens change over time and antibodies have to adapt and learn – the antibodies change over time as a consequence of their adaptation, but the system preserves its choerence and your identity); The human immune system (community of cells named antibodies that repel or destroy antigens – Antigens change over time and antibodies have to adapt and learn – the antibodies change over time as a consequence of their adaptation, but the system preserves its choerence and your identity); Central Nervous System (many neurons of different forms that interact in a complex network – the network adapts and learns- the behavior of the CNS depends on the interactions among neurons instead of the individual actions – billions of interactions among neurons create the capability of mammalians to anticipate – The network of interactions produces an internal model) Central Nervous System (many neurons of different forms that interact in a complex network – the network adapts and learns- the behavior of the CNS depends on the interactions among neurons instead of the individual actions – billions of interactions among neurons create the capability of mammalians to anticipate – The network of interactions produces an internal model)

16. 16 CAS: a summary Choerence in the face of changes Choerence in the face of changes Extensive interactions…………… Extensive interactions…………… ……… among many different constituents …………………. ……… among many different constituents …………………. ………….that produce aggregate behaviors different from the sum of local behaviors……………… ………….that produce aggregate behaviors different from the sum of local behaviors……………… …………and that adapt or learn …………and that adapt or learn

17. 17 Generative Social Science and The Agent-Based Approach

18. 18 The generative approach The generativist’s question: How could the decentralized local interactions of heterogeneous and autonomous agents generate the given macroscopic regularity? The ABM is well suited to the study of this question due to the specific characteristics:  Heterogeneity (diversity)  Autonomy (no top-down control)  Local Interaction (agents interact with their neighbors)  Bounded rationality (bounded information, limited computing power)  Space of action (a space of interconnected resources) (Epstein and Axtell, 1996) The generativist’s question: How could the decentralized local interactions of heterogeneous and autonomous agents generate the given macroscopic regularity? The ABM is well suited to the study of this question due to the specific characteristics:  Heterogeneity (diversity)  Autonomy (no top-down control)  Local Interaction (agents interact with their neighbors)  Bounded rationality (bounded information, limited computing power)  Space of action (a space of interconnected resources) (Epstein and Axtell, 1996)

19. 19 The generative approach A generative experiment consists in (Epstein and Axtell, 1996):  placing an initial population of heterogeneous and autonomous cognitive agents in a virtual environment;  let them interact and evolve according to some individual behavioral rules (micro-specifications);  observe if such micro-specifications are sufficient to generate expected or plausible macroscopic regularities. (Shelling, 1978; Axelrod, 1995; Conte, 1999; Epstein and Axtell, 1996; Holland and Miller, 1991; Tesfation, 2001) A generative experiment consists in (Epstein and Axtell, 1996):  placing an initial population of heterogeneous and autonomous cognitive agents in a virtual environment;  let them interact and evolve according to some individual behavioral rules (micro-specifications);  observe if such micro-specifications are sufficient to generate expected or plausible macroscopic regularities. (Shelling, 1978; Axelrod, 1995; Conte, 1999; Epstein and Axtell, 1996; Holland and Miller, 1991; Tesfation, 2001)

20. 20 The generative approach An ABM provides a computational demonstration that a given micro-specification is sufficient to generate a macroscopic regularity; An ABM provides a computational demonstration that a given micro-specification is sufficient to generate a macroscopic regularity; The generativist wants to know how such macroscopic configuration can be reached by a decentralized system of heterogeneous autonomous agents; The generativist wants to know how such macroscopic configuration can be reached by a decentralized system of heterogeneous autonomous agents; If a micro-specification is able to generate a macroscopic regularity then it is a good candidate to explain it. If a micro-specification is able to generate a macroscopic regularity then it is a good candidate to explain it.

21. 21 Topics and Applications Topics and Applications  Physics  Biology  Living Systems  Economics  Social and Organizational Systems (Social Simulation) Journals Journals  Journal of Artificial Societies and Social Simulation (listed in SSCI)  E:CO (Emergence, Complexity and Organizations)  Computational and Mathematical Organization Theory  IEEE Transactions on Evolutionary Computation Topics and Journals

22. 22  Santa Fe Institue (www.santafe.edu) www.santafe.edu  Swarm Development Group Wiki (Center for the study of Complex Systems-Michigan University- www.swarm.org) www.swarm.org  Center for Connected Learning and Computer Based Modeling (Northwestern University- ccl.northwestern.edu)  Computational Finance and Economic Agents (Essex University- ccfea-research@essex.ac.uk) Scientific Associations and Research Groups

23. 23 The generative approach in doing science Simulation does not demostrate theorems 1. Induction starts from empirical world to discover patterns and rules in a given sets of data 2. Deduction starts from some axioms and proves the consequences that can be derived from these assumptions 3. Simulation is the third way of doing science. Simulation starts from a set of assumptions like deduction, but does not prove theorems. Simulation generates a set of data that can be analyzed inductively. Unlike typical induction, simulation does not work on a set of empirical data and starts from a given set of specified rules.

24. 24 Basic components of ABModels

25. 25 Components of an ABM Autonomous cognitive agents as members of a relational network and operating in a virtual environment by interacting with both resources, constraints and other agents Agents Environment a landscape of renewable resources and environmental constraints Rules Heterogeneity Autonomy Bounded rationality Rules

26. 26 Agents Reactive Agents: they don’t have any symbolic internal model of external world and react to external stimuli- they don’t predict and are able to answer to well known situations (not dynamic environments); Reactive Agents: they don’t have any symbolic internal model of external world and react to external stimuli- they don’t predict and are able to answer to well known situations (not dynamic environments); Deliberative Agents: they are intelligent agents and have an internal symbolic model through which they can predict (low efficiency); Deliberative Agents: they are intelligent agents and have an internal symbolic model through which they can predict (low efficiency); Hybrid Agents: they integrate the efficiency of reactive agents and the intelligence in dynamic environments of deliberative ones – they act instinctively in standard situations and deliberate in dynamic scenarios. Hybrid Agents: they integrate the efficiency of reactive agents and the intelligence in dynamic environments of deliberative ones – they act instinctively in standard situations and deliberate in dynamic scenarios. Agent Somebody or something who acts A means through which an action is carried out or caused

27. 27 Deliberative- Cognitive Agents Agent Somebody or something who acts A means through which an action is carried out or caused Cognitive agents: BDI Beliefs Desires (objectives) Intentions (plans) Action

28. 28 Environment a landscape of renewable resources and environmental constraints The results obtained through a social computation critically depend on the metaphor we choice to represent the environment (coding): topology resources movement agents localization …

29. 29 The steps to build an ABM Identify classes of agents Identify classes of agents Define the characteristics of the space of action (e.g. topology, resources, constraints) Define the characteristics of the space of action (e.g. topology, resources, constraints) Make hypothesis about individual behavioral rules (microspecifications) Make hypothesis about individual behavioral rules (microspecifications) –(if then ) Choose a platform to implement your meta-model Choose a platform to implement your meta-model Write a software code to implement and simulate the model (to perform generative what if analysis and build your own virtual laboratory) Write a software code to implement and simulate the model (to perform generative what if analysis and build your own virtual laboratory)