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Minds and Machines

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30.05.2019 | Original Article

Qualitative Models in Computational Simulative Sciences: Representation, Confirmation, Experimentation

verfasst von: Nicola Angius

Erschienen in: Minds and Machines | Ausgabe 3/2019

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Abstract

The Epistemology Of Computer Simulation (EOCS) has developed as an epistemological and methodological analysis of simulative sciences using quantitative computational models to represent and predict empirical phenomena of interest. In this paper, Executable Cell Biology (ECB) and Agent-Based Modelling (ABM) are examined to show how one may take advantage of qualitative computational models to evaluate reachability properties of reactive systems. In contrast to the thesis, advanced by EOCS, that computational models are not adequate representations of the simulated empirical systems, it is shown how the representational adequacy of qualitative models is essential to evaluate reachability properties. Justification theory, if not playing an essential role in EOCS, is exhibited to be involved in the process of advancing and corroborating model-based hypotheses about empirical systems in ECB and ABM. Finally, the practice of evaluating model-based hypothesis by testing the simulated systems is shown to constitute an argument in favour of the thesis that computer simulations in ECB and ABM can be put on a par with scientific experiments.

Vorheriger Artikel Cognitive Architecture, Holistic Inference and Bayesian Networks

Nächster Artikel The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence

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Computational models in ABM are usually identified with the simulative programs which, by assigning different values to the program’s variables, can be acknowledged as quantitative models. Section 2.2 will nonetheless consider computational models in ABM to be models of the simulative programs which, as it will be extensively shown, are qualitative in nature.

This categorization is taken from Brim et al. (2013).

Other kinds of computational models include continuous-time and discrete-time Markov chains (Yang and Ko 2012), characterized by discrete variables, stochastic rules and, respectively, continuous or discrete time. One reason to focus on purely qualitative models such as KSs is that, at the state of the art, they allow for a better analysis of their state space and therefore most of current research in ECB focuses on them.

Temporal logics express how systems evolve over time and are interpreted over state transition systems. LTL assumes that time is linear, that is, at each state, only one successor state is considered.

This known fact led some, especially Winsberg (2010), to argue that the processes of verification and validation, although conceptually distinct, cannot always be distinguished in the practice of computer simulations.

CTL and CTLK assume that time has a branching structure: at each state (instant of time), distinct computational paths are considered. This is expressed using path quantifiers in CTL and CTLK formulas, requiring that the given statement holds in all paths (A) or in at least one path (E) starting from some specified or initial state.

In connection with agent-based models expressed as reactive systems, simulation becomes necessary for exploratory purposes, i.e. when one observes the running program to detect behaviours of interest but no advanced hypotheses have to be evaluated.

Given a set \(i \in \{ 1,...,n \}\) of agents, a model T for the n agents is introduced as the set-theoretic structure \((S, \pi , P_i, P_n)\) defined by a set S of states denoting possible worlds for agents; an interpretation function \(\pi\) mapping, for each \(s \in S\), from a set of proposition \(\Phi\) to truth values and describing each possible world; a binary relation \(P_i \subseteq S \times S\) defined for states in S and for each agent \(1 \le i \le n\). \(P_i = (s_i, s_j)\) is the possibility relation expressing that agent i considers world \(s_j\) as possible on the basis of the knowledge she possesses at world \(s_i\), for any \(s_i, s_j \in S\). The environment is often modelled as an agent \(n + 1\) outside the set 1, ..., n of agents (Fagin et al. 2004, ch. 4).

The possibility relation \(P_i\) allows KSs for agent-based models to provide an interpretation for the epistemic operator \(K_i\). \(K_i \phi\) holds true iff agent i regards proposition \(\phi\) to be true in all worlds she considers possible. Given a KS T, a world \(s_i\), and a formula f, \(T, s_i \models f\) means that f is satisfied by structure T at world \(s_i\). The semantics for the \(K_i\) operator can thus be formally given as: \((T, s_i) \models K_i f\) iff \((T, s_j) \models f\) for all \((s_i, s_j) \in P_i.\)

It is a discussed topic in the debate on the philosophical novelty of computer simulations whether the problem of maximizing trading-off properties also characterises scientific modelling. For instance, the philosophical analysis of idealization in science deals, among other things, with the problem of maximizing simplicity and precision in scientific models (Weisberg 2007).

In the context of the philosophy of computer science, it has been even argued that the representational relation holding between a KS and the specified program is that of empirical adequacy as understood by Van Fraassen (1980) (Angius and Tamburrini 2011).

Measurement as well as other epistemological and methodological problems affecting experiments are not discussed here in that they are common to any scientific experiment and they have been deeply analysed by the epistemology of scientific experiments (Franklin 1990).

The problem of whether there are pure exploratory experiments in science, that is, experiments carried out without any theoretical presupposition, is object of philosophical debate in the epistemology of scientific experiments (Franklin 1989). Here, scientific experiments are taken to be theory-laden at least in the weaker sense of theory-guidance proposed by (Godfrey-Smith 2009, ch. 5), namely when there exists a theory that guides the experiments in choosing which elements of the experimented system to observe.

Ammann, P., & Offutt, J. (2016). Introduction to software testing. Cambridge: Cambridge University Press.CrossRef

Angius, N. (2013). Abstraction and idealization in the formal verification of software systems. Minds and Machines, 23(2), 211–226.CrossRef

Angius, N., & Tamburrini, G. (2011). Scientific theories of computational systems in model checking. Minds and Machines, 21(2), 323–336.CrossRef

Baier, C., Katoen, J.-P., & Larsen, K. G. (2008). Principles of model checking. Cambridge: MIT press.MATH

Boureanu, I., Kouvaros, P., & Lomuscio, A., (2016). Verifying security properties in unbounded multiagent systems. In Proceedings of the 2016 international conference on autonomous agents & multiagent systems (pp. 1209–1217). International Foundation for Autonomous Agents and Multiagent Systems.

Brim, L., Češka, M., & Šafránek, D., (2013). Model checking of biological systems. In M. Bernardo, E. de Vink, A. Di Pierro, & H. Wiklicky (Eds). Formal Methods for Dynamical Systems. Lecture Notes in Computer Science, 7938, (pp. 63–112). Berlin, Heidelberg: Springer.MATH

Cederman, L.-E. (2001). Agent-based modeling in political science. The Political Methodologist, 10(1), 16–22.

Clarke, E., Grumberg, O., Jha, S., Lu, Y., & Veith, H., (2000). Counterexample-guided abstraction refinement. In E. Allen Emerson, & A. P. Sistla (Eds). Computer aided verification. Lecture Notes in Computer Science, 1855, (pp. 154–169). Berlin, Heidelberg: Springer.

Clarke, E. M., Grumberg, O., & Peled, D. (1999). Model checking. Cambridge: MIT press.MATH

Cooper, R. P., & Guest, O. (2014). Implementations are not specifications: Specification, replication and experimentation in computational cognitive modeling. Cognitive Systems Research, 27(Supplement C), 42–49.CrossRef

Crooks, A., Castle, C., & Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6), 417–430.CrossRef

Crooks, A., Hudson-Smith, A., & Dearden, J. (2009). Agent street: An environment for exploring agent-based models in second life. Journal of Artificial Societies and Social Simulation, 12(4), 10.

Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds). Agent-based models of geographical systems (pp. 85–105). Dordrecht: Springer.CrossRef

Durán, J. M. (2013). A brief overview of the philosophical study of computer simulations. APA Newsletter on Philosophy and Computers, 13(1), 38–46.

Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: social science from the bottom up. Massachusetts: Brookings Institution Press.CrossRef

Fagin, R., Halpern, J. Y., Moses, Y., & Vardi, M. (2004). Reasoning about knowledge. Cambridge: MIT press.MATHCrossRef

Fisher, J., Harel, D., & Henzinger, T. A. (2011). Biology as reactivity. Communications of the ACM, 54(10), 72–82.CrossRef

Fisher, J., & Henzinger, T. A. (2007). Executable cell biology. Nature Biotechnology, 25(11), 1239.CrossRef

Fisher, J., Piterman, N., Hubbard, E. J. A., Stern, M. J., & Harel, D. (2005). Computational insights into caenorhabditis elegans vulval development. Proceedings of the National Academy of Sciences of the United States of America, 102(6), 1951–1956.CrossRef

Franklin, A. (1989). The neglect of experiment. Cambridge: Cambridge University Press.

Franklin, A. (1990). Experiment, right or wrong. Cambridge: Cambridge University Press.CrossRef

Frigg, R., & Reiss, J. (2009). The philosophy of simulation: Hot new issues or same old stew? Synthese, 169(3), 593–613.MathSciNetCrossRef

Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist. New Delhi: McGraw-Hill Education.

Gillespie, D. T. (1992). A rigorous derivation of the chemical master equation. Physica A: Statistical Mechanics and its Applications, 188(1–3), 404–425.CrossRef

Godfrey-Smith, P. (2009). Theory and reality: An introduction to the philosophy of science. Chicago: University of Chicago Press.

Guala, F. (2002). Models, simulations, and experiments. In L. Magnani, & N. J. Nersessian (Eds.). Model-based reasoning: Science, technology, values (pp. 59–74). Boston, MA: Springer. CrossRef

Guetzkow, H. S., Kotler, P., & Schultz, R. L. (Eds.) (1972).Simulations in Social and Administrative Science: Overview and Case-Examples. Englewood Cliffs, N.J.: Prentice-Hall.

Hartmann, S. (1996). The world as a process: Simulation in the natural and social sciences. In R. Hegselmann, U. Muller, & K. Troitzsch (Eds.), Modelling and simulation in the social sciences from the philosophy of science point of view (pp. 77–100). Dordrecht: Kluwer Academic.CrossRef

Hughes, R. I. (1997). Models and representation. Philosophy of Science, 64, S325–S336.CrossRef

Hughes, R. I. (1999). The ising model, computer simulation, and universal physics. Ideas in Context, 52, 97–145.

Humphreys, P. (1994). Numerical experimentation (pp. 103–121). Patrick Suppes: Scientific Philosopher.

Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. Oxford: Oxford University Press.CrossRef

Johnson-Laird, P. N. (1980). Mental models in cognitive science. Cognitive Science, 4(1), 71–115.CrossRef

Kollman, K., Miller, J. H., & Page, S. E. (1992). Adaptive parties in spatial elections. American Political Science Review, 86(4), 929–937.CrossRef

Kouvaros, P., & Lomuscio, A. (2015). Verifying emergent properties of swarms. In IJCAI (pp. 1083–1089).

Kreft, J.-U., Booth, G., & Wimpenny, J. W. (1998). Bacsim, a simulator for individual-based modelling of bacterial colony growth. Microbiology, 144(12), 3275–3287.CrossRef

Kröger, F., & Merz, S. (2008). Temporal logic and state systems. Berlin: Springer.MATH

Kuhn, T. S. (1970). The structure of scientific revolutions. Chicago: University of Chicago press.

Lee, W., Pardo, A., Jang, J.-Y., Hachtel, G., & Somenzi, F. (1996). Tearing based automatic abstraction for ctl model checking. In Computer-aided design, 1996. ICCAD-96. Digest of technical papers., 1996 IEEE/ACM International Conference on (pp. 76–81). IEEE.

Lenhard, J., & Winsberg, E. (2010). Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 41(3), 253–262.CrossRef

Lomuscio, A., Qu, H., & Raimondi, F. (2009). Mcmas: A model checker for the verification of multi-agent systems. In International conference on computer aided verification (pp. 682–688). Springer.

Magnani, L. (2004). Model-based and manipulative abduction in science. Foundations of Science, 9(3), 219–247.CrossRef

Magnani, L., Nersessian, N., & Thagard, P. (1999). Model-based reasoning in scientific discovery. New York: Kluwer Accademic.CrossRef

Morgan, M. S., et al. (2002). Model experiments and models in experiments (pp. 41–58). Model-based reasoning: Science, technology, values.

Morrison, M. (2009). Models, measurement and computer simulation: the changing face of experimentation. Philosophical Studies, 143(1), 33–57.CrossRef

Morrison, M., & Morgan, M. (1999). Models as mediators. New York: Cambridge University Press.

Newell, A., & Simon, H. A. (1961). Computer simulation of human thinking. : JSTOR.

Norton, S., & Suppe, F. (2001). Why atmospheric modeling is good science (pp. 67–105). Changing the atmosphere: Expert knowledge and environmental governance.

Parker, W. S. (2009). Does matter really matter? Computer simulations, experiments, and materiality. Synthese, 169(3), 483–496.CrossRef

Pylyshyn, Z. W. (1986). Computation and cognition: Toward a foundation for cognitive science. Cambridge: The MIT Press.

Schaub, M. A., Henzinger, T. A., & Fisher, J. (2007). Qualitative networks: A symbolic approach to analyze biological signaling networks. BMC Systems Biology, 1(1), 4.CrossRef

Suárez, M. (Ed.). (2008). Fictions in science: Philosophical essays on modeling and idealization. London: Routledge.

Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87(3), 449–508.MathSciNetCrossRef

Symons, J., & Horner, J. (2014). Software intensive science. Philosophy & Technology, 27(3), 461–477.CrossRef

Thagard, P. (1984). Computer programs as psychological theories (pp. 77–84). Mind: Language and Society.

Van Fraassen, B. C. (1980). The scientific image. Oxford: Oxford University Press.CrossRef

Weisberg, M. (2007). Three kinds of idealization. The Journal of Philosophy, 104(12), 639–659.CrossRef

Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. Cambridge: MIT Press.

Winsberg, E. (1999). Sanctioning models: The epistemology of simulation. Science in Context, 12(2), 275–292.CrossRef

Winsberg, E. (2010). Science in the age of computer simulation. Chicago: University of Chicago Press.CrossRef

Winsberg, E. (2015). Computer simulations in science. In E. N. Zalta (Ed.), The stanford encyclopedia of philosophy. Metaphysics Research Lab, Stanford University. (Summer 2015 ed.)

Wooldridge, M. (1997). Agent-based software engineering. IEE Proceedings-Software, 144(1), 26–37.CrossRef

Yang, H.-T., & Ko, M. S. (2012). Stochastic modeling for the expression of a gene regulated by competing transcription factors. PloS ONE, 7(3), e32376.CrossRef

Titel: Qualitative Models in Computational Simulative Sciences: Representation, Confirmation, Experimentation
verfasst von: Nicola Angius
Publikationsdatum: 30.05.2019
Verlag: Springer Netherlands
Erschienen in: Minds and Machines / Ausgabe 3/2019
Print ISSN: 0924-6495
Elektronische ISSN: 1572-8641
DOI: https://doi.org/10.1007/s11023-019-09503-9

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