Nuno Cruz David (ISCTE, Lisbon, Portugal) Nuno.David@iscte.pt

1. The Role of Intentional Decision-Making in the Context of Complex Systems and Information Technologies Nuno Cruz David (ISCTE, Lisbon, Portugal) Nuno.David@iscte.pt Jaime Simão Sichman (University of São Paulo, Brazil) Helder Coelho (University of Lisbon, Portugal)

2. Social Science Simulation Among the issues confronted by computer science is the extent to which formal and empirical methods are sufficient to secure the goals of the discipline After the consolidation of the multiagent paradigm, computer simulation became the fundamental tool for analysing societies as complex systems However, the experimental character of social science simulation remains ambiguous

3. AimsThe Rules of the Game in Social Science Simulation Characterize the logic of the method of simulation, bearing in mind theencounter between the formal and empirical methods of computer science with the interpretative methods of the social sciences: An additional perspective about the way we can understand the concepts of program and computation Computational phenomena are intentional phenomena and this is particularly manifest in social science simulation with agent-based models

4. Structure of the Argument • The meaning of formal in computer science and simulation • The role of programming languages in social science simulation • Intentional verification of programs(see David et al., 2005, to appear next month in JASSS, special issue on Epistemological Perspectives on Simulation) • The need for participative simulation in the context of decision making with information technologies

5. What are the Rules of the Game in Simulation? Current Characterization in Scientific Practice is Unclear, if not Misleading E.g.: Pitfalls: Implications of Agent-Based Modeling for Social Theory: Is agent-based computation a process of deductive formal inference? Is it possible a more unified Social Science through simulation? Is it appropriate to apply simulation to assess concrete socioeconomic and environmental/ecological problems with a status of “empirical” research? Are computerprograms verified empirically?

6. formal The Kind of Scientific Knowledge that Simulation is Providing The Fallacies of Formal Computation in the Literature Formal control: “no messy problems of missing data or uncontrolled variables unlike in experimental or observational studies” (Axelrod, 1997, p.27) Formal explanation: informal theories don’t have explanative power: “the social sciences may become sciences in a strict methodological sense only by basing them on formal models” (Kluver et al. 2003; Epstein, 1999) kinds of knowledge in computer science Formal Deduction through Execution (FDE): sociocultural algorithms (e.g. Kluver et al. 2003)

7. Another Fallacy: The “Intuition” Argument The Intuition Argument of Robert Axelrod (1997) “Simulation is a third way of doing science. Like deduction, it starts with a set of explicit assumptions. But unlike deduction, it does not prove theorems. Instead, a simulation generates data that can be analyzed inductively. Unlike typical induction, however, the simulated data comes from a rigorously specified set of rules rather than direct measurement of the real world. While induction can be used to find patterns in data, and deduction can be used to find consequences of assumptions, simulation modeling can be used as an aid intuition”.

8. FDE Argument in the Literature Generative social science: since “(...) every agent-based computation can be executed by a suitable register machine (...) for every agent-based computation, there is a corresponding logical deduction (...) each run is itself a deduction (...)” (Epstein, 1999, In Complexity, v.4, n.5, our enphasis) Generative sufficiency: agent-based models provide formal demonstrations that a given microspecification is suficient to generate a macrostructure of interest (Epstein, 1999) However! In computer science: A classic debate confronting researchers advocating the use of formal methods for verifying programs with those advocating the use of empirical methods (see Hoare, Dijkstra, Ardis et al. (1989), Pleasant (1989), Paulson et al. (1989), Bevier et al. (1989), Fetzer, (1989)).

9. Objection to the FDE Argument (I) 1) Ontological confusion: DFE conflates the terms “computation” and “execution” 2) Methodological contradiction: an unexpected result can be a reflection of a mistake in the programming (bug) or a consequence of the model itself

10. Objection to FDE (II) Reduction to the Formal Verification Project - Specification F1:I1O1 and a program P1 as text that can be read, edited, printed - Computation of P1 denoted by P1(I1) and the execution of P1 denoted by P1(I1) - Suppose that P1(I1)=O1 (partial correctness) and P1(I1)=O2 - According to DFE there is a specification F2:I1O2 and some program P2 such that P2(I1)=O2 - So there is a specification F2:I1O2 such that the execution of P1 and the computation of P2 satisfies F2 - Absurd: The formal verification project is possible: the behaviour of P1 execution (as well as P2 computation) necessarily corresponds to F2:I1O2

11. Formal vs. Empirical in Computer Science Again Computer programs and scientific theories have a semantic significance that (pure) mathematical proofs do not possess But even scientific theories do not possess the causal capabilities of computer programs, which can affect the performance of computers when they are loaded and executed James Fetzer (1988;1999)

12. Proofs, Theories and Programs James Fetzer (1988;1999)

13. Verification of Programs in Simulation For the classical theory of computation, program verification ascertains the validity of outputs as function of inputs, regardless of any interpretation given in terms of any theory or any phenomenon not strictly computational Program execution: an automatic process of deductive formal inference, which is verified empirically

14. The Role of Programming Languages Embedded Models PROGRAMS MACHINES Iconographic Machine (Model Z) Iconographic Level Program(Model E) kinds of knowledge in computer science HIGH LEVEL High Level Program(Model P) Abstract Machine(Model S) empirical formal Abstract Machine (Model B) Low Level Program(Model p) LOW LEVEL Target Machine

15. Lack of Expresiveness Relative consistency between abstract machines is tested against the behaviour of the program However: Often the behaviour of simulations is described in terms of representations that cannot be expressed by first order logics: E.g. Culture dissemination, influence, friendship, innovation, state nations, political actors, friendship, etc.

16. Example: The Bit-Flipping Mechanism Bit-flipping: if two agents share different cultural values then the values converge If the meaning of the rule was not presented to the observer, could he inquire empirically the program and find out that the agents follow this or that rule? 74271 87274 34872 98392 3849338493 89293 29384 39203 8994093948 38283 28383 92383 93939 33998 38287 93948 92377 5473373948 88584 83920 72333 34383 74271 87274 34872 98392 3849338493 89393 29384 39203 8994093948 38283 28383 92383 93939 35998 38257 93948 92377 5473373948 88584 83920 72533 34383 iteration n iteration n+1

17. Empirical and Intentional Verification of Programs The intentional meaning of the original rules surpasses the causal meaning of the new rules, insofar as the interpretation of the original ones is not the result of a process of empirical verification Since the expressiveness of the specification languages cannot be captured by a first-order language, then the kind of knowledge that can be known about computer programs should not be considered empirical

18. Empirical and Intentional Verification of Programs The causal link: simulation platforms, compilers, interpreters Empirical verification: The interpretation of the behaviour of program executions in terms of contingent conditions of necessity The intentional link: the implementer Intentional Verification: The interpretation of the behaviour of program executions and the social target in terms of contingent conditions of intentionality

19. Causal and Intentional LinksE.g. The Schelling Model The causal link: simulation platforms, compilers, interpreters Empirical content: “There is a critical value for parameter C, such that if it is above this value the grid self-organises into segregated areas of single colour counters. This is lower than a half”. (Edmonds, 2000) The intentional link: the implementer Intentional content: “Even a desire for a small proportion of racially similar neighbours might lead to self-organised segregation” (Edmonds, 2000)

20. Multiparadigmatic Character of Social Science Simulation kinds of knowledge in computer science The perspective of intentional computation reflects the multiparadigmatic character of social science in terms of agent-based computational social science intentional empirical formal

21. Conclusions Distinction between empirical and intentional verification of programs reflects a distinction in the kind of experimental knowledge that can be known about social simulations Only in the context of some limited community of observers can a specification and a program be considered a set of sufficient conditions to explain the behaviour of a simulation The importance of intention in information technologies The role of iconographic programming languages The social scientist methodological context and the socioeconomic context of stakeholders Participative simulation in the context of decision making with information technologies – Science as critical thinking in a democratic context

22. Related References David, Nuno; Sichman, Jaime; Coelho, Helder (2005). “The Logic of the Method of Agent-Based Simulation in the Social Sciences: Empirical and Intentional Adequacy of Computer Programs”. Accepted to Journal of Artificial Societies and Social Simulation (JASSS). Draft version available at http://www.iscte.pt/~nmcd/pub/logicJASSS.htm David, Nuno; Sichman, Jaime; Coelho, Helder (2005). “Intentional Adequacy of Computer Programs as the Experimental Reference of Agent-Based Social Simulation”. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'05, Utrecht University, Netherlands, 25 - 29 July. David, N. (2005). “Verificação Empírica e Intencional de Programas em Simulação Social Baseada em Agentes”, Tese de Doutoramento (PhD), Universidade de Lisboa (in portuguese). Fetzer, J (1988). Program Verification: The Very Idea. Communications of the ACM, v.31, pp. 1048-1063. Fetzer, J (1999). The Role of Models in Computer Science. The Monist, v.82, n.1 (General Topic: Philosophy of Computer Science), La Salle, pp. 20-36.