Sistemi per la Gestione Aziendale. AA. 2016-17 Ingegneria Gestionale (LM) Introduzione ai Complex Adaptive Systems Sistemi per la Gestione Aziendale - Proff Giuseppe Zollo Cristina Ponsiglione

Complexity Non linearity 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) Non linearity

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

Emergence - Self Organization Examples of CAS Emergence - Self Organization

Autonomy and etherogeneity – local interactions Examples of CAS Autonomy and etherogeneity – local interactions

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); 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)

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

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); 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); Continual Adaptation (agents continuously adapt) ; 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).

CAS properties Autonomous and heterogeneous agents Local and massive interactions Non linearity Emergence from the bottom-up Self-organization Learning and adaptation

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

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

The Use of Simulation 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 research approach to analyze complex adaptive systems

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 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)

The generative approach 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; If a micro-specification is able to generate a macroscopic regularity then it is a good candidate to explain it. Exploration of assumptions and not prediction