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)

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

AimsThe Rules of the Game in Social Science

Simulation

Characterize the logic of the method of simulation, bearing in mind the encounter 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

Structure of the Argument

1) The meaning of formal in computer science and simulation

2) The role of programming languages in social science simulation

3) Intentional verification of programs(see David et al., 2005, to appear next month in JASSS, special issue on Epistemological Perspectives on Simulation)

4) The need for participative simulation in the context of decision making with information technologies

What are the Rules of the Game in Simulation?

Is agent-based computation a process of deductive formal inference?

Is it appropriate to apply simulation to assess concrete socioeconomic and environmental/ecological problems with a status of “empirical” research?

Are computer programs verified empirically?

Is it possible a more unified Social Science through simulation?

Current Characterization in Scientific Practice is Unclear, if not Misleading

E.g.: Pitfalls: Implications of Agent-Based Modeling for Social Theory:

The Kind of Scientific Knowledge that

Simulation is Providing

The Fallacies of Formal Computation in the Literature

kinds of knowledge in computer science

formal

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)

Formal Deduction through Execution (FDE): sociocultural algorithms (e.g. Kluver et al. 2003)

Another Fallacy: The “Intuition” Argument

“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 Unlike typical induction, however, the

simulated data comes from a rigorously specified set of rules rather simulated data comes from a rigorously specified set of rules rather than direct measurement of the real world.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”.

The Intuition Argument of Robert Axelrod (1997)

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)).

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

Objection to FDE (II)

- Specification F1:I1O1 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:I1O2 and some program

P2 such that P2(I1)=O2- So there is a specification F2:I1O2 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:I1O2

Reduction to the Formal Verification Project

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)

Proofs, Theories and Programs

MathematicalProofs

ScientificTheories

ComputerPrograms

Syntactic Entities yes yes yes

Semantic Significance no yes yes

Causal Capability no no yes

James Fetzer (1988;1999)

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

The Role of Programming Languages

HIGH LEVEL

LOW LEVEL

PROGRAMS MACHINES

High Level Program(Model P)

Low Level Program(Model p)

Abstract Machine(Model S)

Abstract Machine(Model B)

Iconographic Level Program

(Model E)

Iconographic Machine (Model Z)

Target Machine

kinds of knowledge in computer science

empirical

formal

Embedded Models

Lack of Expresiveness

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.

Relative consistency between abstract machines is tested against the behaviour of the program

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Example: The Bit-Flipping Mechanism

iteration n

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iteration n+1

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?

Bit-flipping: if two agents share different cultural values then the values converge

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

Empirical and Intentional Verification of Programs

The interpretation of the behaviour of program executions in terms of contingent conditions of necessity

The interpretation of the behaviour of program executions and the social target in terms of contingent conditions of intentionality

The intentional link: the implementer

Intentional Verification:

The causal link: simulation platforms, compilers, interpreters

Empirical verification:

Causal and Intentional LinksE.g. The Schelling Model

“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)

“Even a desire for a small proportion of racially similar neighbours might lead to self-organised segregation” (Edmonds, 2000)

The intentional link: the implementer

Intentional content:

The causal link: simulation platforms, compilers, interpreters

Empirical content:

Multiparadigmatic Character of Social Science Simulation

kinds of knowledge in computer science

intentional

empirical

formal

The perspective of intentional computation reflects the multiparadigmatic character of

social science in terms of agent-based computational social science

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

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.