Lucio Biggiero Università dell’Aquila, Knownetlab Research Center

1. Lo studio dei sistemi complessi attraverso la simulazione ad agenti interagenti:prospettive applicative nelle scienze sociali e in ecologia Lucio Biggiero Università dell’Aquila, Knownetlab Research Center www.knownetlab.it, lucio.biggiero@gmail.com Lucio Biggiero 2009

2. An overview of ABSM • 1) What ABSM are • 2) Some epistemological and methodological aspect • 3) Some categorization of simulation methods • 4) A categorization and example of ABSM • 5) Some suggestions for future works Lucio Biggiero 2009

3. 1) What ABSM are • Artificial laboratories for generative experiments • Computer programs, that is more or less complex algorithms • They are more than tools: they are theories/models • They are experimental laboratories that can provide social scientists with the same tools of natural scientists Lucio Biggiero 2009

4. A historical perspective Artificial intelligence (50’-60’) Artificial life (70’-…) Artificial societies (80’-…) biology Social sciences Computer science Laboratories for Lucio Biggiero 2009

5. General Traits of ABSM • They can be theory laden • They can be data laden • They can be empirically testable • ABSM Are Theories in Action into the Virtual World • ABSM lead social sciences from dogma and doxa to episteme Lucio Biggiero 2009

6. They “reproduce” reality • In principle, they have no limit to reproducibility • In practice, every model should be purposefully built, and its complexity seriously hinders its viability • Its algorithmic nature guarantees for the internal consistency Lucio Biggiero 2009

7. The principle of generative explanation (Epstein, 2006) • If (1) the model of a given macro-phenomenon can be implemented through plausible theoretical hypotheses concerning agents, their interactions and environment; • If (2) there is likelihood between the simulation outcomes and the empirical findings; • Then those theoretical hypotheses can be considered as sufficient conditions to explain that phenomenon Lucio Biggiero 2009

8. 2) Some epistemological and methodological aspect Lucio Biggiero 2009

9. Properties of social phenomena • Because of very high phenomenological complexity, they require a huge size of empirical base necessary to build or test theories • Phenomenological complexity: instability, roles of expectations, emergent properties, sensitivity to interactions between observer-observed, path dependency, nonlinearity, high interconnectedness between factors Lucio Biggiero 2009

10. Which kind of phenomena are better suited for ABSM? • Many complex agents interacting in complex ways: the micro-macro issue • Autonomous cognitive agents • Agents characterized by multiple (even contradictory) behaviors (and hence their preferences should not be forcedly described by expected subjective utility functions) • Equilibrium is not a must Lucio Biggiero 2009

11. Human traits of agents • Perception-distinction capability; • Intentionality; • Goal-seeking; • Memory (information storing); • Heterogeneity; • Communication; • Rule creation-following; • Cheating. Lucio Biggiero 2009

12. Social complexity • Emphasis on interactions respect to matters • Emphasis on complex and often unexpected outcomes produced by simple rules • Reduced role for collective minds (deliberated social rules) • Weakening of the links between intentionality and its outcomes Lucio Biggiero 2009

13. Ideal methodological process Field research (in vivo) Laboratory cases (in vitro) Current knowledge Simulation model (in virtuo) Lucio Biggiero 2009

14. 3. Some categorization of various types of simulation models • top-down vs. bottom-up (emergence) • static vs. dynamic • function-based vs. system-based • many low complex parts vs. few highly complex parts Lucio Biggiero 2009

15. Examples • Econometric models • Structural equation models • Network analysis • Neural nets • Cellular automata • NK-FL • Game theories • System dynamics • Agent-based models Lucio Biggiero 2009

16. Which Kind of Simulations?An Epistemological View • Inner logical structure: once described agents’ behavioral characteristics, what should we expect they did or will do after n iterations (interactions)? • Depending on the model purpose, structure and validation chance, its outcome can range from scenario analysis totestable predictions and retro-dictions Lucio Biggiero 2009

17. In practice • If we know the goal, we use models to search for the better patterns (behaviors) to reach it • If we don’t know the goal, but we know the current patterns, we use models to study long term evolution • If we know both, we can use models to explore what happens with different goals or different patterns or both Lucio Biggiero 2009

18. 4. Some categorization and example of ABSM Lucio Biggiero 2009

19. Categorizations • Three broad categories of in terms of level of abstraction/generalization: • 1) case studies; • 2) middle range models; • 3) abstract models. • First- and second-order emergentist • Merely rules applying or even creating • With or without (and levels of) learning Lucio Biggiero 2009

20. Case studies • Modeling a specific entity • The aerospace industrial cluster of Rome in the 2005 • The FIAT case in 2008 • Etc. • Understanding Anasazi culture change through agent-based modeling • Dean et al., 2000. in Kohler & Gumerman (eds.) Dynamics in human and primates societies: agent-based modeling for social and spatial processes. Oxford: Oxford UP. Lucio Biggiero 2009

21. Anasazi culture change • Ethnic group living between 1800 and 1200 BC • They suddenly disappeared: why? • The traditional explanation addressed to climate changes • Conversely, the model suggests social-political factors Lucio Biggiero 2009

22. Abstract models • They point at very general issues, which are common to many fields and/or do not depend significantly on specific circumstances • Example: the emergence of cooperation between people, insects, virus, molecules. See literature on direct and indirect reciprocity, the evolution of cooperation, etc. Lucio Biggiero 2009

23. Schelling’s model (1971, 1978) on racial segregation in American cities • Black and white agents are randomly placed on a grid whose cells represent filled or empty households • For levels of the tolerance threshold at or above 0.3, an initially random distribution of households segregates into patches of black and white, with households of each color clustering together Lucio Biggiero 2009

24. Opinion dynamics orthe fragility of democracy How can opinions, which are initially considered as extreme and marginal, manage to become the norm in large parts of a population? Deffuant et al., 2002 i.e. the Nazis, the Bolshevists, the Maoists, the radical Islamists, the ecologists?, etc. growth and dominance Lucio Biggiero 2009

25. A simple model structure • Agents have an opinion (a real number between -1 and +1) with a certain degree of uncertainty and interact randomly • Agent j is affected by the opinion of agent i by an amount proportional to the difference between their opinions, multiplied by the amount of overlap divided by agent i’s uncertainty, minus 1 • Excepted few extremists with most positive or negative certain opinions, most agents start with an opinion taken from a uniform random distribution and with a common level of uncertainty Lucio Biggiero 2009

26. Outcomes and predictions • Under these conditions extremism spreads leading the population towards one of the two opposite extremes • Without extremists, the population would converge on the moderate opinions Lucio Biggiero 2009

27. Abstract models • The COD (Coordination for Organization Design) Model (Biggiero & Sevi, 2009) • Middle range models • The CIOPS (Cognitive Inter-organizational Production System)Model (Biggiero & Sevi, 2009) • The KNOWTIC (Knowledge Transfer within Industrial Clusters) Model (Biggiero & Basevi, 2009) Lucio Biggiero 2009

28. The COD model Emergent effects of task interdependence and bounded rationality on workgroup performance Task interdependencies • Parallel • Sequential • Reciprocal Lucio Biggiero 2009

29. The simulation model The model structure Coordination Looking and engaging Agent Agent Agent Lucio Biggiero 2009

30. Main results simple people perform better when coordinated by simple rules Effective combination among Bounded rationality and coordination complexity high effective ineffective Coordination complexity ineffective effective low low high Computational capacity Lucio Biggiero 2009

31. …main results we obtained… an algorithmic confirmation of the law of requisite variety; an algorithmic confirmation of the ordering of interdependencies in terms of complexity; an algorithmic confirmation of the fit between task interdependencies and coordination mechanisms; a formalization of task interdependencies and bounded rationality in terms of computational capacity; an algorithmic analysis of the combined effects of bounded rationality, task interdependencies and coordination mechanisms on workgroup performance Lucio Biggiero 2009

32. Suppliers’ quality Quality of purchases Based on Information transfer Goal of clients: choosing the best suppliers Profits of clients Selection Devices 1. Random Choice 2. Direct Experience 3. Indirect Experience 4. Reputation Opportunism by cheating and its effects on industry profitability Lucio Biggiero 2009

33. Sequential Technology market 1: downstream / intermediate Market 2: intermediate / upstream Producer 1 Producer A Supplier 1 Supplier α Producer B Supplier 2 Supplier β Producer 2 Producer 3 Producer C Supplier 3 Supplier γ Producer D Supplier 4 Supplier δ Producer 4 segment 1: downstream segment 2: intermediate segment 3: upstream Filiere and market structure Lucio Biggiero 2009

34. Business relationships n5 n2 n3 n3n1 n4 n4 cognitive network n3 cognitive network n4n2 n3n5 n4n3 n3n2 n4n1 n4 perception of n1 Attributes: - Quality - Reliability as informer sources: - direct experience-based trust - indirect experience-based trust - reputation-based trust Interactions between the structural and the cognitive networks Industry n1 Lucio Biggiero 2009

35. Honest final producers – general results • DEBT produces a worse performance than INDEBT and REBT • REBT ensures more stability and higher average profitthan INDEBT • reliable communication makes easier and faster the information space exploration. Lucio Biggiero 2009

36. Final producers – Cheating effects on REBT Rebt.0: agents do not cheat Rebt.50: agents cheat half times Rebt.1: agents always cheat • When all agents are full cheaters (REBT.1), profitability oscillates around a profit that is near, and sometimes below, that produced by RND • when only false information are shared and firms rely on reputation suggestions, agents cognitive efforts are totally wastedand the worst performance is observed. Lucio Biggiero 2009

37. Effectiveness of decision making patterns • When agents cheat, it is better to trust direct than indirect experience in order to avoid false information. • Information reliability (quality) is more strategic than its quantity. Honest agents Cheating agents High High REBT INDEBT DEBT DEBT INDEBT REBT Effectiveness Low Low Lucio Biggiero 2009

38. REBT.0 RND REBT.5 REBT.1 First Tiers Final producers vs. Cheating agents in REBT • Even if they are submitted to the same cost structure, mechanisms and threats, First Tiers explore a smaller part of information space • It ensures a similar profitability when agents do not cheat, and a greater profitability when they cheat Lucio Biggiero 2009

39. The KNOWTIC Model Spontaneous dynamics or policy interventions Competitiveness at organizational, inter-organizational, and cluster level ? A set of structural and behavioral variables K creation and transfer Successo competitivo Industrial cluster • How are tacit and explicit knowledge created, cumulated and transferred between organizations (firms and centers of research). • What is the role of innovation and imitation? • What is the role of bounded rationality? • Etc. Lucio Biggiero 2009

40. Some research hypotheses H1: La capacità di assorbimento permette di colmare il gap conoscitivo esistente tra due distretti/cluster industriali. H2: La capacità di assorbimento influenza le strategie di investimento in ricerca e sviluppo. H3: La capacità di assorbimento permette di ottenere risultati vantaggiosi anche in presenza di costi di R&D elevati. H4: Una distribuzione di capacità di assorbimento tra le ditte di un cluster che segue una funzione di potenza (80/20) produce più conoscenza di altre distribuzioni. Lucio Biggiero 2009

41. The virtual experiments to test the first hypothesis Quantità di conoscenza posseduta da due distretti industriali H1s2 e H1s3 nell’arco di 100 intervalli. Quantità di conoscenza posseduta da due distretti industriali H1s2 e H1s3 nell’arco di 100 intervalli. Quantità di conoscenza posseduta da due distretti industriali H1s4 e H1s5 nell’arco di 100 intervalli L’ipotesi di ricerca risulta essere confermata. E’ possibile colmare il gap conoscitivo esistente tra due distretti/cluster industriali attraverso investimenti volti ad incrementare la capacità di assorbimento, la quale incrementa in maniera esponenziale i suoi benefici in presenza di investimenti in ricerca e sviluppo elevati. Per testare questa ipotesi vengono condotte 5 simulazioni facendo variare tre fattori: Lucio Biggiero 2009

42. Se riflettiamo onestamente e attentamente, la maggior parte delle cose che insegniamo e che non è stata ottenuta per via sperimentale (reale o virtuale) è nel migliore dei casi vera ma non si sa veramente perché altrimenti falsa Lucio Biggiero 2009

43. 5) Some suggestions for future works • Esistono delle norme individuali (micro-behavior) e semplici in grado di indurre fenomeni sociali (macro-behavior) di sviluppo sostenibile? • Esistono delle norme individuali (micro-behavior) e complicate o forti in grado di indurre fenomeni sociali (macro-behavior) di sviluppo sostenibile? Lucio Biggiero 2009

44. Esistono delle norme individuali (micro-behavior) e semplici in grado di scoraggiare fenomeni sociali (macro-behavior) di sviluppo sostenibile? • Esistono delle norme individuali (micro-behavior) e complicate o forti in grado di scoraggiare fenomeni sociali (macro-behavior) di sviluppo sostenibile? Lucio Biggiero 2009

45. In ciascuno dei 4 casi precedenti, quali sono i fattori chiave? Le soglie? Le preferenze individuali? Le asimmetrie informative? La razionalità degli agenti? Le loro interdipendenze? Lucio Biggiero 2009

46. Che cosa cambia se si introduce un attore collettivo? Che caratteristiche deve avere per essere efficace? Che ruolo gioca il grado di (de)centralizzazione decisionale? Che ruolo gioca il grado di (de)centralizzazione della produzione (o del consumo) di energia, materie prime, cibo, ecc.? Lucio Biggiero 2009

47. Che ruolo gioca la differenziazione (economica, sociale, geografica, culturale, ecc.) dei produttori e dei consumatori? Esistono scale effects? Does topology matter? Che ruolo giocano opportunismo, attitudine cooperativa, ecc.? Lucio Biggiero 2009

48. La simulazione ad agenti è lo strumento ideale e il più appropriato per tutti gli studi di scenario e di policy Lucio Biggiero 2009