Abstract
Having entered into the problem structuring methods, system dynamics (SD) is an approach, among systems’ methodologies, which claims to recognize the main structures of socio-economic behaviors. However, the concern for building or discovering strong philosophical underpinnings of SD, undoubtedly playing an important role in the modeling process, is a long-standing issue, in a way that there is a considerable debate about the assumptions or the philosophical foundations of it. In this paper, with a new perspective, we have explored theory of knowledge in SD models and found strange similarities between classic epistemological concepts such as justification and truth, and the mechanism of obtaining knowledge in SD models. In this regard, we have discussed related theories of epistemology and based on this analysis, have suggested some implications for moderating common problems in the modeling process of SD. Furthermore, this research could be considered a reword of system dynamics modeling principles in terms of theory of knowledge.
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Notes
A useful definition of “falsificationism” appears in the Dictionary of Jargon (Green 1987, London: Routledge and Kegan Paul): “(Sociology) a doctrine which claims that scientific advance can only come through testing and falsifying hypotheses, which are then replaced by new hypotheses to be tested and falsified in their turn; one can only falsify, never ultimate verify.”
Critical rationalists believe scientific theories, and any other claims to knowledge, can and should be rationally taken to task, and (if they have empirical content) can and should be subjected to tests which may falsify them.
By relativism, these authors mean some sort of relativism, in which the epistemic justification of models is relative to the interests and purposes of the participants in the model building process, not an extreme relativism that assumes knowledge is an objective representation of reality and that theory justification can be an objective, formal process.
“ For IR, it does not make much sense to suppose a passive reality, one which is ready-made and structured independently for our knowledge and actions, from which our models can at best be copies. The same is true of SD, where knowledge from mental models is such an important part of the building and justification of the final SD models.” (Vazquez et al. 1996).
“A mental model of a dynamic system is a relatively enduring and accessible, but limited, internal conceptual representation of an external system whose structure maintains the perceived structure of that system.” Doyle and Ford (1998).
“ According to Searle, social and institutional phenomena are construed through the recursive iteration of three basic mechanisms: collective intentionality, the assignment of functions and systems of constitutive rules. These phenomena are epistemologically objective but ontologically subjective and, in general, we only construe them implicitly.” (Vazquez and Liz 2007: p. 18).
“ According to Brandom, logic does not describe or represent any ideal realm. It has an expressive role linked to what is implicit in our inferential practices.” (Vazquez and Liz 2007: p. 18).
Refer to: Olaya 2009: pp. 9057–9087.
For more details about the kinds of SD models, refer to: (Barlas (1996), pp. 199–201).
For review of The SD modeling process across the classic literature, see: Luna-Reyes and Andersen (2003).
A set of graphs and other descriptive data showing the development of the problem over time.
Causes and Effects in SD models are variables, and a causal connection is the relation between them. In rewording SD models to theory of knowledge expressions, as it will be explained, “beliefs in variables” is intended, and subsequently, we count causal connection among variables as justification (Although justification has a meaning beyond causal connection, but it could be said that any causal connection of beliefs of knowledge is a kind of justification).
We should declare a point of our epistemological analysis of SD models in order to decrease, somewhat, misunderstanding about the precise similarities of justification and SD modeling. The similarity which we use in our paper may be construed as a kind of “argument from analogy”, whereby perceived similarities are used as a basis to infer some further similarity that has yet to be observed.
Of course, the argument doesn’t state that the two things are identical (or interchangeable), but it says that they are only similar. The argument may provide us with good evidence for the conclusion and explanation, but the conclusion does not follow as a matter of logical necessity.
Accordingly, we don’t have authority to use this argument as a pretext for complete substitution of expressions and concepts as they are originated in different systems and their implications are different, in essence. Let us to clarify this position by an example from mathematics: equivalence of numbers, say number 2, in two-dimensional space is the size of a line equal to 2 , while we couldn’t never substitute 2 in two dimensional space with a line and use them instead of each others.
It should be noted that here, we mean a wide meaning of justification, which covers descriptive and prescriptive ones.
In a classification, beliefs are divided into basic and non-basic beliefs. “A belief is basic for a person at a time just in case it is not based on any other belief for the person at that time. Basic beliefs are at the foundations of a person’s doxastic structure” (Quinn 2002: p. 526). Or in other words, beliefs which are fully justified independently of the support of any other beliefs.
This linearity is not against the nonlinear attribute of complex problems. Otherwise, it is defined as circular structure, which is named linear against holism in coherentism debate in theory of knowledge.
There are some other problems and challenges regarding the necessity of coherentism in belief justification that say, under special conditions, we may have some justified beliefs that are incoherent with other beliefs. The lottery paradox is an example of this kind. Assume a fair lottery with a thousand tickets in it. Each ticket is so unlikely to win that we are justified in believing that it will lose. So, we can infer that no ticket will win. However, we know that some ticket will win.
Explanation of this answer is out of the scope of this paper, for more information, refer to: (Pojman 2001).
Plantinga offers the summary statement of his account of warrant in the following reference: Plantinga, Warrant and Proper Function [hereafter WPF] (New York: Oxford University Press, 1993, pp. 46–47).
see also DeRose 1992: p. 914.
“Holon dynamics” and “Modeling as radical learning” are two types of SD models that could be covered in this critique:
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“Holon Dynamics (or HD) is an envisaged form of practice grounded in the interpretivist paradigm. With HD the notion of model building as a social process is embraced and models are nominalist representations, useful devices which help human agents to create their social worlds via debate and the construction of shared meaning.” (Lane 1999: p. 517).
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“Modeling as Radical Learning implies the use of SD modeling to further communicative competence within groups.” (Lane 1999: p. 518).
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Protagoras (c.490-c.420 BC) “Fragments,” in H. Diels and W. Kranz (eds) Die Fragmente der Vorsokratiker (Fragments of the Presocratics), Berlin: Weidmann, 7th ed, 1954, vol. 2, pp. 253–71. (The standard collection of the ancient sources both fragments and testimonia, the latter designated with “A” , includes Greek texts of the fragments with translations in German.) Routledge Encyclopedia of Philosophy, 1998.
“Traditional epistemology, with its focus on the analysis of knowledge, is relatively silent about the questions of belief dynamics. If there is talk about belief change, it is generally assumed that it takes place on the basis of learned evidence that is certain. Traditional epistemology shares this assumption with logical theories of belief revision such as the AGM theory (Gardenfors and Rott 1995).
However, Jeffrey taught us that learning often does not come in the form of certainties. To address these cases of learning and belief change, philosophers as well as researchers in artificial intelligence have formulated new updating rules (such as Jeffrey conditionalization) and developed powerful tools such as the theory of Bayesian networks (Neapolitan 2003)”. (Dancy et al. 2010: pp. 101–102).
There are three different belief changes as: belief expansion(when we learn something new), belief revision (in the light of evidence that contradicts what we had earlier mistakenly accepted) and belief contraction (when we discover that the reasons for some of our beliefs are invalid) that is usual in modifying the primary hypothesis about a complex problem in SD.
Forrester (1961: p. 60) argues in this respect that “[a]ll constants and variables of [a system dynamics] model can and should be counterparts of corresponding quantities and concepts in the actual system”.
Also, Sterman (2000, 517) said that: “All variables and relationships should have real world counterparts and meaning”.
There is no privileged single model for a complex behavior and we could discover structures, which are generating the same behavior with no primary evidence for prefering among them (Olaya 2009: 9067).
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Acknowledgments
The authors would like to thank Mohammadmahdi Soleimanipour and Mohsen Feyzbakhsh for their support and ideas in revising our article. We also thank the reviewers of Foundations of Science for their useful comments and suggestions that help us to clarify and enrich our ideas better. We also appreciate Professor Aerts, Editor-in-Chief of the journal for his efforts in the process of submission to acceptance of the article.
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Zolfagharian, M., Akbari, R. & Fartookzadeh, H. Theory of Knowledge in System Dynamics Models. Found Sci 19, 189–207 (2014). https://doi.org/10.1007/s10699-013-9328-9
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DOI: https://doi.org/10.1007/s10699-013-9328-9