theory of knowledge in system dynamics models

Found SciDOI 10.1007/s10699-013-9328-9

Theory of Knowledge in System Dynamics Models

Mohammadreza Zolfagharian · Reza Akbari ·Hamidreza Fartookzadeh

© Springer Science+Business Media Dordrecht 2013

Abstract Having entered into the problem structuring methods, system dynamics (SD) isan approach, among systems’ methodologies, which claims to recognize the main structuresof socio-economic behaviors. However, the concern for building or discovering strong philo-sophical 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 orthe philosophical foundations of it. In this paper, with a new perspective, we have exploredtheory of knowledge in SD models and found strange similarities between classic epistemo-logical concepts such as justification and truth, and the mechanism of obtaining knowledgein SD models. In this regard, we have discussed related theories of epistemology and basedon this analysis, have suggested some implications for moderating common problems in themodeling process of SD. Furthermore, this research could be considered a reword of systemdynamics modeling principles in terms of theory of knowledge.

Keywords System dynamics · Theory of knowledge · Justification · Belief · Truth

1 Introduction

In any discipline, there are always a number of underlying philosophical predispositions inthe projects of scientists the reflection on which may enlighten the debates on the conceptsand the processes in the discipline,as a result, it will ensure its vibrance in overcomingnew problems and improving its capability against potential competitors. Furthermore, it is

M. Zolfagharian (B) · R. AkbariImam Sadiq University, Chamran Highway, Tehran, Irane-mail: [email protected]

R. Akbarie-mail: [email protected]

H. FartookzadehMalek Ashtar University of Technology, Babayi Highway, Lavizan, Tehran, Irane-mail: [email protected]

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essential to comprehend the implications, the limitations, and the scope of our very scientificpractice (Olaya 2009: p. 9057). System dynamics (Hereafter SD) as a systems’ methodologyfor modeling socio-economic problems is not an exception. The necessity of the explanationof these issues in SD will be more apparent if we take a look at the increasing numberof modelers who have SD in their toolkits without any awareness of the potential effects ofphilosophical background on model conceptualization, formal model building, validation andpolicy design. Furthermore, remaining un-answered questions about these predispositions fordeveloping this methodology neccesiate a more profound and a broader research on this scopein order to reinforce protective belt of SD against the existing criticisms.

However, in this regard, during more than 50 years since the emergence of SD, effortshave been made and different philosophical approaches have been proposed as theoreticalfoundations of SD. Sometimes, for instance, it has been suggested that certain varieties ofmoderate realisms such as that of Popper’s falsificationism1 or critical rationalism,2 couldfit into SD procedures. Since causal models in SD offer clear test points by which problemscould be solved and also theory could be advanced (Bell and Bell 1980; Bell and Senge 1980).Other times, it has been maintained that a certain practical relativism,3 or a contextualist andpragmatist philosophy of science along the lines of Kuhn, would offer an adequate frame-work for the justification of the claims of SD models (Barlas and Carpenter 1990). “Internalrealism”4 of Hilary Putnam is another philosophy, whose proposers have believed that it canbe adapted to the roles of mental models5 and, in general, conceptual problems in SD models(Vazquez et al. 1996). Recently there have been some efforts for using Constructivist of JohnSearl6 and Expressivist of Robert Brandom7 for explaining philosophical foundations of SD(Vazquez and Liz 2007). They “have argued that a certain combination of the perspectivesof Searle and Brandom could be very useful to achieve a reflective understanding of SDmodelling. [Vazquez and Liz] applied constructivist and expressivist views in the discussionof three crucial problems concerning the validation of SD models: the ontological prob-lem of realism with respect to the structures postulated in SD models, the epistemological

1 A useful definition of “falsificationism” appears in the Dictionary of Jargon (Green 1987, London: Routledgeand Kegan Paul): “(Sociology) a doctrine which claims that scientific advance can only come through testingand falsifying hypotheses, which are then replaced by new hypotheses to be tested and falsified in their turn;one can only falsify, never ultimate verify.”2 Critical rationalists believe scientific theories, and any other claims to knowledge, can and should be ratio-nally taken to task, and (if they have empirical content) can and should be subjected to tests which may falsifythem.3 By relativism, these authors mean some sort of relativism, in which the epistemic justification of modelsis relative to the interests and purposes of the participants in the model building process, not an extremerelativism that assumes knowledge is an objective representation of reality and that theory justification can bean objective, formal process.4 “ For IR, it does not make much sense to suppose a passive reality, one which is ready-made and structuredindependently for our knowledge and actions, from which our models can at best be copies. The same is trueof SD, where knowledge from mental models is such an important part of the building and justification of thefinal SD models.” (Vazquez et al. 1996).5 “A mental model of a dynamic system is a relatively enduring and accessible, but limited, internal conceptualrepresentation of an external system whose structure maintains the perceived structure of that system.” Doyleand Ford (1998).6 “ According to Searle, social and institutional phenomena are construed through the recursive iterationof three basic mechanisms: collective intentionality, the assignment of functions and systems of constitutiverules. These phenomena are epistemologically objective but ontologically subjective and, in general, we onlyconstrue them implicitly.” (Vazquez and Liz 2007: p. 18).7 “ According to Brandom, logic does not describe or represent any ideal realm. It has an expressive rolelinked to what is implicit in our inferential practices.” (Vazquez and Liz 2007: p. 18).

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problem of the explanatory value of SD models, and the methodological charge of merelyproducing a kind of “patchwork”. Finally, [they] generalized that constructivist and expres-sivist approach analysises the interrelations between mental models, social systems and SDmodels”. (Vazquez and Liz 2007: p. 18).

Furthermore, there were some efforts for proposing a theoretical framework that canconvey all SD activities (e.g. Lane 1999 and Pruyt 2006). In this regard, Lane unearthsdifferent forms of SD practices in Burrels and Morgan framework for explaining socialtheories of system dynmaics and Pruyt classifies different SD practices in a paradigmaticclassification framework on the basis of their basic assumptions, namely, Positivist systemdynamics, Post positivist System Dynamics, Critical pluralist system dynamics, Pragmatistsystem dynamics, Constructivist system dynamics, Transformative Emancipatory, Criticalsystem dynamics.

Although, all of these articles have some points about the philosophical issues of SD and asa result, explicitly or implicitly, indicate notes on the epistemology or the nature of knowledgein SD models, but just a few of them consider the kowledge elicited from SD models justifiedtrue belief (the standard analysis of knowledge in epistemology)8 and based on that establishtheir theoretical analysis, which could open a new arena for the epistemological analysisof SD. Therefore, in this paper, falling back upon to the point that any model offers someknowledge around a problem, an epistemological analysis of SD models, based on justifiedtrue belief concept, is discussed. This will bring significant implications for the structure andvalidation of SD models and could moderate the debates of researchers about the commonproblems in the justification of SD models. To this end, beginning with a brief account of SD,and theory of knowledge and its core concepts (namely, justification and truth) and debatesconcerning them, it goes on to describe these concepts in SD models. It proceeds to suggestsome implications for acquiring stronger and more valid knowledge following each category.

2 System Dynamics

System dynamics is an approach, which has entered into the scientific and practical discus-sions in 1950s by J.W. Forrester, the professor of Massachusetts Institute of Technology. Hesaw the emergence of SD for university research in management as “the same opportunity toassume aggressive, innovative leadership that we have seen in the great schools of medicine,science, and engineering.” (Forrester 1961: p. 360).

From the late 1950s to the late 1960s, SD was applied almost exclusively to corporate andmanagerial problems and its conceptual and practical framework was explained in two booksof “Industrial Dynamics” and “Principles of Systems”. In 1968, SD has started experiencingnew major projects such as “Urban Dynamics” and “World Dynamics” in different versionsthat drew world-wide attention toward viewing complex problems through the lens of SD.Nowadays, researchers from different fields have examined the capabilities of SD in facingthe complexities of diverse problems and building simulation models (including quantitativeand qualitative practical, Theoretical, interactive and learning models9) in the areas includingbusiness, economics, society, politics and culture and etc. .

8 Refer to: Olaya 2009: pp. 9057–9087.9 For more details about the kinds of SD models, refer to: (Barlas, 1996, pp. 199–201).

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Fig. 1 Modeling is an iterative learning process (Sterman 2000, p.87)

2.1 System Dynamics Modeling Process

In describing modeling process, system dynamicists have organized stages varying in labeland arrangement but the activities considered along the different stages remain fairly constantacross them.10 Sterman (2000), in his explanation for modeling process, stated five iterativestages, namely, problem articulation, dynamic hypothesis, formulation, testing and policyformulation and evaluation that ensure two main functions of SD as the explanation of thestructure of complex socio-economic problems and policy making for improving the currentsituation of the surveyed problem (Fig. 1).

It could be said that the most important claim of SD is mapping and changing the mentalmodels of the clients (or the problem owners) in the process of modeling. However, thisprocess could lead to the learning (with the aim of supporting reasoning, team learning,and nurturing system thinking and planning based on scenario-making) and improving someundesirable performance patterns.

In this section, it will be tried to explain these stages, briefly, so that it prepares a commoncontext for discussing epistemological issues of SD.

1. Problem articulation (boundary selection)

The most important step is complex problem articulation. It proceeds from clarifyingthe clients’ problem by going through the evidences and accessing to the stems of the realproblem that brings about a vicious whirlpool in the activity boundaries of the problemowners. This will continue by eliciting and structuring the mental models of the group to definea problem. Establishing behavior reference modes11 and setting appropriate time horizon aretwo most useful processes, which modelers try to use for this end in SD modeling.

2. Formulating a Dynamic Hypothesis

After problem articulation, modellers should start developing a theory about the problem-atic behavior that can be obtained in conversation and interaction with clients. This theoryshows the process which is rendered by the problem and usually, it is provisional, because it

10 For review of The SD modeling process across the classic literature, see: Luna-Reyes and Andersen (2003).11 A set of graphs and other descriptive data showing the development of the problem over time.

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may be changed (by expansion, revision and contraction of variables or loops) as you learnin the modeling process. In early stages of formulation, modellers should capture mentalmodels of clients and develop maps of causal structure to recapitulate them by facilitationtools (such as Model boundary diagrams, Subsystem diagrams, Causal loop diagrams, Stockand flow maps, and Policy structure diagrams) for endogenous explanation of the complexproblem. (Sterman 2000: pp. 94–102).

3. Formulation of a Model

Until now, the reasoning base of SD has been established, and to contiue, modelers shouldtry to complete designed mental models via mathematization of variable relations and sim-ulation facilities on the behavior of the proposed structure system in various scenarios. Thisstep helps the modeler and clients to understand the flaws of proposed behaviour structureand see their endogenous explanation profoundly and insightfully.

4. Testing

Like all other models, system dynamics’ functions would be valuable if it is validatedcorrectly. Testing SD models is a continuous process which should begin in the early stagesof modelling for building confidence in models (Barlas and Carpenter 1990). In summary, itcould be said that there are some structural and behavioral experiments that modellers set totest the model. Absolutely, this process will be different, depending on the selected validationapproach.

5. Policy Design and Evaluation

From this step, the modeler tries to go far from the current situation by scenarios andpolicies that include the creation of entirely new strategies, structures, and decision rules.In policy making, the robustness of policies and their sensitivity to uncertainties in modelparameters and structure and mutual effects of alternative policies must be assessed to proposebest ways for improvements (Sterman 2000: pp. 103–104).

3 System Dynamics and Theory of Knowledge

Epistemology, or its contemporary equivalent theory of knowledge, includes the most pivotaland central philosophical issues that are concerned with the nature and the scope (limitations)of knowledge. Much of the debate in this field has focused on analyzing the nature of knowl-edge and how it relates to connected notions such as truth, belief, and justification. It alsodeals with the mechanisms of producing knowledge, as well as skepticism about differentknowledge claims.

Epistemologists have distinguished some species of knowledge, including: propositionalknowledge (that something is so), nonpropositional knowledge of something (for instance,knowledge by acquaintance, or by direct awareness), empirical (a posteriori) propositionalknowledge, nonempirical (a priori) propositional knowledge, and knowledge of how to dosomething.

Despite controversies over distinctions between such species(namely, the relations and theviabilities among them) (Moser and VanderNat 2002: p. 3), it could be said that the episte-mologist’s primary interest is propositional knowledge (or knowledge of truths) (Klein 1998:p. 2005). The generally accepted meaning of propositonal knowledge (Hereafter knowledge)among Western epistemologists, from Plato to the midst of twentieth century, was “justifiedtrue belief”, which is, sometimes, called the standard analysis of knowledge.

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In epistemological analysis of SD modeling, we could find the tracks of all aforementionedknowledge in different phases of the modeling process. For example, the sort of knowledge ofthe problem owners, used in the process of modeling especially in the problem articulation,could be taken into account as knowledge of things, knowledge of that and knowledgeby acquaintance. On the other hand, the knowledge proposed for improving actions anddecision is a kind of knowing-how. According to system dynamicists, SD models are as thereflections of our beliefs about the networks of causes and effects, and each model offerssome knowledge around a problem; in other words, SD modelers look for the justificationof “beliefs in variables” about a specific problem to reach knowledge with some levels oftruth.12 Therefore, it could be said that although all types of knowledge elicited or obtained inthe process of SD modeling are important, the explanation of propositional knowledge in SDmodels needs about more analysis that could bring more distinctive theoretical and practicalimplications for system dynamicists and epistemologists. In continue, we will concentrate onthe mechanism of obtaining this type of knowledge according to the SD modeling process.The rest of the paper proceeds to explore the concepts, namely, justification structure and truthfor rewording system dynamics modeling process in term of theory of knowledge, accordingto discovered similarities.13

4 Justification

In epistemologists’ view, knowledge is related to how our beliefs are possible or how theyare formed. In response to this question, the theories of justification14 comes to the debatesfor analysis of knowledge. These theories could be classified in the following categoriesaccording to the structure and the content of knowledge they propose:

1. Based on structure, there are three distinct justification theories:

a) Foundationalism

Aristotle, St. Thomas Aquinas and Rene Descartes are classical foundationalists. Accord-ing to the classical foundationalist philosophers, we have infallible, direct, self-evident and

12 Causes and Effects in SD models are variables, and a causal connection is the relation between them. Inrewording SD models to theory of knowledge expressions, as it will be explained, “beliefs in variables” isintended, and subsequently, we count causal connection among variables as justification (Although justificationhas a meaning beyond causal connection, but it could be said that any causal connection of beliefs of knowledgeis a kind of justification).13 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 usein our paper may be construed as a kind of “argument from analogy”, whereby perceived similarities are usedas 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 thatthey 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 expres-sions 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, intwo-dimensional space is the size of a line equal to 2 , while we couldn’t never substitute 2 in two dimensionalspace with a line and use them instead of each others.14 It should be noted that here, we mean a wide meaning of justification, which covers descriptive andprescriptive ones.

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Fig. 2 Schematic view offoundationalism Structure. It isadopted from the site: http://www.csus.edu/indiv/g/gaskilld/intro/epistemology3.htm,at 27/3/13

incorrigible knowledge of the first principles or basic statements from which other knowledgeis elicited from them.15 You can see a schematic view of this kind of structure above (Fig. 2):

As it shows, through taking infallible principles, which are comprised of the first principlesor intuitions, we can build a strong structure of knowledge. Despite some of the structuralbenefits of foundationalism, some problems challenge this structure, such as the “short con-tents of knowledge” that can be produced by infallible statements. This point can lead to theskepticism, which may cause serious problems for the house of knowledge. It is worth men-tioning that foundational processes work on logical, perceptual, memorial, and testimonialbeliefs (Pojman 2001: p. 127).

b) Coherentism

Coherence theories of truth state that truth resides in the Absolute System of Knowledge.This kind of justification structure predominates in theoretical beliefs such as natural, reli-gious, political, social, economic, and metaphysical beliefs (Pojman 2001: p. 127). To explainit, two most basic theories of coherentism as linear and holistic will be detailed.

Based on linear16 coherentism, a belief (B1) gets its justification from belief B2, which,in turn, gets its support from other beliefs, namely, B3, B4, . . ., Bn , which, itself, will bejustified by B1. It is clear that the regress problem and the circular scheme remain in thisstructure because none of them has any positive epistemic warrants (Fig. 3).

Considering holistic coherentism, one belief does not receive its positive epistemic statusonly from other beliefs; rather its status is related to the role it plays in a total system ofbeliefs. Suppose a three-dimensional network in which each belief is a connecting node, heldtogether by other beliefs. As opposed to foundationalism, justification and protecting beliefsare multi-sided and concurrent, and each belief may be supported by many beliefs, not justone belief occurring in the linear structure. In the following, you can see the symbol of thiskind of justification structure. As it is clear in the graph, a variable such as A1 is justified byA6, A7, A2 and the A1, itself, justify A3, A4 (Fig. 4).

Against the foundationalism, in which the process of eliciting knowledge from the princi-ples is obvious, in this structure, a question that may be asked is whether or not coherence is

15 In a classification, beliefs are divided into basic and non-basic beliefs. “A belief is basic for a personat a time just in case it is not based on any other belief for the person at that time. Basic beliefs are at thefoundations of a person’s doxastic structure” (Quinn 2002: p. 526). Or in other words, beliefs which are fullyjustified independently of the support of any other beliefs.16 This linearity is not against the nonlinear attribute of complex problems. Otherwise, it is defined as circularstructure, which is named linear against holism in coherentism debate in theory of knowledge.

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http://www.csus.edu/indiv/g/gaskilld/intro/epistemology3.htm




Fig. 3 Justification in linearstructure

B1

B2

B3 ......

Bn

B4

Fig. 4 Justification in holisticstructure

A4

A1 A2

A3

A5A6

A7 A8

a necessary and sufficient condition for justification. In literature, some classical criticismshave been offered against the sufficient condition, the most important of which are indicatedin the following17:

1. The Alternative System Objection

This problem is related to the truth connection of coherent systems. Coherence, accordingto Rescher (1973), pays attention solely to “the strictly internal relationships of implicationthat obtain within” a set of mutually cohering propositions. So, “it is problematic, to saythe least, to show that a relationship obtains between this feature” of such propositions “andtheir actual truth status”. Therefore, there is no criterion for preferring alternative systems ofbeliefs that are coherent.

2. The Input Objection

This objection which is, also, called “the isolation objection” said that coherentism, byitself, does not have any criterion for relating it to the external world. Laurence BonJour(1985: p. 108), clearly, formulates this objection as follows:

17 There are some other problems and challenges regarding the necessity of coherentism in belief justificationthat 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 ticketis 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.

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Coherence is purely a matter of the internal relations between the components of thebelief system; it depends in no way on any sort of relation between the system of beliefsand anything external to that system. Hence if, as coherence theory claims, coherenceis the sole basis for empirical justification, it follows that a system of empirical beliefsmight be adequately justified, indeed might constitute empirical knowledge, in spite ofbeing utterly out of contact with the world that it purports to describe. Nothing aboutany requirement of coherence dictates that a coherent system of beliefs need receiveany sort of input from the world or be in any way causally influenced by the world.

3. The Infinite Regress Objection

The summary of this critique is that in order to justify one belief, it should be justifiedto person S and its consistency should be surveyed with all the beliefs of person S, and thissurvey will lead to “the infinite regress objections” as follows:

A: S is my belief set and B1 is coherent with S.A1: S is my belief set and A is coherent with S.A2: S is my belief set and A1 is coherent with S.A3: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..An: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..

In response to these objections, coherentists proposed some points, as mentioned in thefollowing:

For the first objections, it is said that, although we cannot prefer between two differentcoherent systems, we can find out which of them is closer to the truth. Of course, this reasoningis a kind of circular one, since for realizing more coherentism, they use reasoning and viceversa. Also, there are some answers to the lack of input one of which is Bonjure’s statement:“If…”.18 Finally, in response to the third objection, it is said this confuses the process ofdiscovering whether a belief is justified or not, with the status of actually being justified(Pojman 2001: pp. 120–122).

In view of these criticisms and such responses from coherentists and also “short contentsof knowledge” in foundationalism, many philosophers give up pure foundationalism andcoherentism and choose moderate positions. As a result, it is rare to find some philosopherslocated in the extreme of this spectrum.19

2. Based on the content, there are two distinct theories of justification: internalism andexternalism.

a) InternalismFrom the point of view of internalists, justification is relied upon as the essenceof internal beliefs and the intuitive relationship of beliefs, or a combination of theessential and structural characteristics of beliefs’ and non-beliefs’ internal processes.Of course, internal justification has an epistemological meaning, namely, epistemicaccess that means the falsehood and truth of beliefs are intuitive, and are only affectedby the structural characteristics of beliefs, their internal relations, and mental andcognitive states.In this category, we could see foundherentism and classic foundationalism and coher-entism and later on, we would explain foundherentism:

18 Explanation of this answer is out of the scope of this paper, for more information, refer to: (Pojman 2001).19 For example, Audi (1993) and Alston (1976) have tried to establish an approach, namely, moderatefoundationalism.

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a-1) Foundherentism:Foundationalism and coherentism are not exhaustive of possible structures oftheory of epistemic justification. In “Evidence and Inquiry” Haack argues for atheory of the justification of empirical beliefs, namely Foundherentism, whichincludes elements from both foundationalist and coherentist intuitions. Accord-ing to this theory, in order for experience to play a role in justification (thatis also, ignored in coherentism), “it is not required that the basic belief orbeliefs be completely justified independently of any further belief” (Haack 1993:p. 43.). All that is required is that the basic belief or beliefs acquire partial justi-fication via experience and that this partial justification occurs independently ofthe support of any other beliefs (Clune 1997: 461). It should be noted that Hackuses the phrase of “truth indicative” against “truth conducive” for her theory ofjustification, which insures that the subject is ultimately justified in virtue of thejustifiers’ experiences (as an ultimate source of empirical information availableto him (Clune 1997: p. 218).

b) ExternalismAn externalist sees the justification dependent on external and objective criteria. Inthe following, we will review theories, which are based on externalist view:b-1) reliabilism

The most prominent form of externalism is reliabilism, which, in its most com-mon form, holds that a belief is justified if and only if it was produced and orsustained by a reliable belief-forming process (Goldman 1986).

b-2) Naturalism of QuinnMost of today’s naturalized epistemology originates from Quine’s (1969) “Epis-temology Naturalized”. He treats the object of epistemology as a natural phe-nomenon, and calls for an experimental method in the study of epistemologyto “provide an account of a certain natural phenomenon, namely, knowledgeitself.” (Kornblith 1999: p. 161).Naturalist and non-naturalist divide over whether or not continuity betweenepistemology and science exists. Naturalists differ among themselves over whatform this continuity should take. (Maffie 1990: p. 281)

b-3) Warrant of PlantingAfter proposing Gettier counterexample (Gettier 1963) against Justified TrueBelief, Alvin Plantinga’s version of externalism is offered as an account of war-rant, defined as whatever it is that distinguishes knowledge from mere true belief.He rejects the term “justification” as a label for this quality, on the ground that itis biased in favor of internalist conceptions of what is required for knowledge.20

3. Besides these theories, there are two more approaches:

a) ContextualismAccording to DeRose (1999: p.187), “ ‘Contextualism’ refers to the position that thetruth-conditions of knowledge ascribing and knowledge denying sentences… varyin certain ways according to the context in which they are uttered.”21

20 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).21 see also DeRose 1992: p. 914.

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Table 1 Justification of beliefs in an exemplary loop

The general form of causal connections in term of theory of knowledgeA belief in increase (or decrease) of the quantity of a cause variableJustifies a belief in increase (or decrease) of the quantity of an effect variable

belief in increase of budget Justifies belief in increase of sales budget

belief in increase of sales budget Justifies belief in increase of net hiring rate

belief in increase of net hiring rate Justifies belief in increase of sales force

belief in increase of sales force Justifies belief in increase of customer orders

belief in increase of customer orders Justifies belief in increase of order backlog

belief in increase of order backlog Justifies belief in increase of Order fill rate

belief in increase of Order fill rate Justifies belief in increase of Revenue

belief in increase of Revenue Justifies belief in increase of Budget

b) PragmatismIn summary, pragmatism implies that a proposition is acceptable to us if and onlyif it is useful to us besides its approved truth, that is, it is useful to us to accept theproposition.

4.1 Justification in System Dynamics

With respect to the role of mental models in the modeling process and the structure of causalloop diagrams and its direct relation to our beliefs, it will be clear that the dominant structurein these models is coherentism. If, in dynamic hypotheses of SD models, in which thereare no stock and flow variables, and all variables are considered as beliefs (experimentalor logical), we can see linear coherentism among causes and effects of closed loops. Fora better and complete understanding, let us review the famous market growth of Forrester(Forrester 1978). This model is illustrating causal loops, which show a set of relationshipsoften encountered in the growth of a new product. In the phase of problem articulation, themodelers reached a behavior reference mode that reflected a fall in the market growth afterexponential mode. Then, the dynamic hypotheses were constructed in term of causal loopdiagrams one of which is as following. This is attained by mapping the mental models ofproblem owners, which is made of a set of related beliefs that produce knowledge around theproblem.

Justification of beliefs in the sales growing loop is depicted in the Table 1, as can be seen,i.e. belief in increase (or decrease) of budget as a cause variable Justifies a belief in increase(or decrease) of sales budget as an effect variable.

It is clear that beliefs-protecting in this loop is similar to the justification structure of linearcoherentism (Fig. 5).

However, justification structure in SD models does not finish at this point and the modeler(justifier) combines these loops and creates a system and network of coherent beliefs, which islikened to the holistic coherentism. As can be seen in the following model, beliefs justificationand protection is multi-sided and each of them is justified by some belief. As an example,the order backlog variable which is protected by just one belief in the last model, is protectedby variables such as sales force, sales effectiveness and support variables such as revenueand the quantity of desired production (Fig. 6). Of course, you can find larger and morecomplicated SD models in the papers and projects where multi-sidedness of their variablesis more evident.

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Fig. 5 Parameters that affectsales growing loop (Morecroft2007)

budget

revenue

Order fill rate

order backlog

customer orders

sales force

net hiring rate

sales budget

Sales Force

TargetSales Force

Cost per SalesRepresentative

SalesBudget

Backlog

Order Rate

ShipmentRate

Revenue

Price

SalesEffectiveness

RecentRevenue

Change inRecent

Revenue

DesiredProduction

CapacityUtilization

Capacity

+

Sales ForceAdjustment

Time

+

+

++

+

+

++

-

Fraction ofRevenue to

Sales

+

-

RevenueReporting

Delay

-

NormalDelivery

Delay-

+

Sales ForceNet Hiring

Rate

-

+

+

<Effect ofDelivery Delay

on SalesEffectiveness>

<Normal SalesEffectiveness>

<CapacityAcquisition

Delay>

<DesiredCapacity>

Table forCapacity

Utilization

Switch forCapacity

Constraint

Switch forEndogenous

Orders

ExogenousOrder Rate

InitialBacklog

<InitialCapacity>

InitialCapacity

R1

Sales Growth

<ExogenousSales

Effectiveness><Switch forEndogenous

SalesEffectiveness>

Fig. 6 Simple structure of holistic coherentism in system dynamics models (Sterman 2000)

In the following, implications of regarding SD models as justification structure throughthe comparisons of coherence theories’ criticisms in SD models will be discussed:

1. The alternative system objection in SD models occurs in a way that we cannot easilyprefer between two models that survey the dynamics of a specific problem and haveequal structural coherentism. Formal and informal processes of validation in SD modelsseek to decrease this problem, but it seems that because of the kind of SD’s justificationstructure, these models will suffer from this challenge forever.

2. The input problem in SD models is not relevant because of their empirical beliefs. Inreality, each belief of the models (causal variables) is elicited from the experiment and

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the sense that is according to the real world (although we can find some instances of lackof firm relations between beliefs).Nevertheless, there are some models, which are designed based on the subjective reality.In this manner, they have some challenges from this critique.22

3. The infinite regress objections are also not relevant to the SD’s holistic coherentismstructure, since, before the creation of the coherent network of beliefs, some variableshave been chosen and the consistency of the selected and relevant beliefs will be tried tosurvey. It is worthy to note that identifying the model’s boundary and the endogenous,exogenous and excluded variables, is the first and key stage of mapping the systemstructure that could remove the mentioned critique.

Now, the insufficiency of foundationalism structure is also apparent because followingthis structure in social systems, we couldn’t build wide house of knowledge and skepticismis a main threat.

All in all, for moderating these challenges in SD models, a combination of coherentismand foundationalism for the SD model’s justification is suggested (like what Hack proposedas foundherentism, especially for resolving the first problem). As a result, (in addition to thedominant linear and holistic coherentism), some initial and inevitable principles based onpast experience of best practitioners will be accepted as foundations, as a kind of archetypesor even causal relations of beliefs (in variables).

These foundations, considering the high development of SD, could be prepared in cooper-ation with the specialists of SD and the specialists of socio-economic fields as a SD referencehandbook.

In the third step of modeling process of SD, the modelers formulated the causal relationsamong variables, quantitatively, which is beyond our discussions. We will proceed to theforth step, validation, according to the truth concept in epistemology and give up the policymaking for future research, since it is a kind of prescriptive knowledge.

5 Truth

In the tripartite analysis of knowledge, truth is one of the necessary conditions of the beliefsin order to count them as knowledge. Examining beliefs and identifying truth or untruth ofbeliefs is one of the long-lasting concerns of epistemologists. It should be noted that thereare differences between the nature and criteria of truth, while most challenges are related tothe criteria of truth.

There are four important truth theories (correspondence theory, coherence theory, prag-matic theory, and relativist theory). In the following, we will explain these theories and theircriticisms, briefly, and finally, truth theories in SD models, which are related to the validationdebates, will be discussed.

22 “Holon dynamics” and “Modeling as radical learning” are two types of SD models that could be coveredin this critique:

• “Holon Dynamics (or HD) is an envisaged form of practice grounded in the interpretivist paradigm. WithHD 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 ofshared meaning.” (Lane 1999: p. 517).

• “Modeling as Radical Learning implies the use of SD modeling to further communicative competencewithin groups.” (Lane 1999: p. 518).

.

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5.1 The Correspondence Theory of Truth

This interpretation is, likely, to be as one of the oldest general theories about truth and ofcourse one of the most well-known theories that was accepted among Western and Easternphilosophers. In this view, truth consists in its agreement with (or correspondence to) reality.On the other hand, “the correspondence theory captures our Common-sense intuition thattruth depends on something objective (or mind-independent) in the world that makes it true”(Pojman 2001: p. 5).

This theory has attracted many proponents but, traditionally, some kinds of criticisms havebeen leveled at correspondence theories, primarily that all of them are the starting points ofthe tendency to create other truth theories. Most of the objections to these theories are relatedto the ambiguities and difficulties occurring in confirming and verifying beliefs regardingfacts.

5.2 Coherence Theory

“The term ‘coherence’ in the phrase ‘coherence theory of truth’ has never been very pre-cisely defined. The most we can say by way of a general definition is that a set of two ormore beliefs are said to cohere if they ‘fit’ together or ‘agree’ with one another” (Kirkham1998a). Different versions have been proposed in regard to coherence theory; one of the mostimportant belonged to the idealists that emphasized on subjectivity rather than objectivity indiscovering truth of beliefs.

This theory has two principles (Pojman 2001: p. 7):

1. The doctrine of internal relation2. The doctrine of the degrees of truth

According to the doctrine of internal relations, any subset of a structure has the necessaryconsistency with the other subsets of that structure, and the result will identify the structure’struth.

The doctrine of the degrees of truth says that if we suppose pure truth as a whole, it hasnever been attained (because of human cognitive limitations) and just comes in degrees. Inthis manner, structures will be contextually true and not purely true.

The most common objection to the coherence theories is that these theories make truth asa matter of a truth bearer’s relations to other truth bearers rather than its relations to realityand the conditions they place on truth are too weak (Kirkham 1998a).

5.3 The Pragmatic Theory of Truth

Under the name of pragmatic theory, we can see two distinctive types of truth. First, theconsensus theory of C.S. Peirce that says a true proposition is one that would be endorsedunanimously by all persons who had had sufficient relevant experiences to judge it. Second,there is the instrumentalist theory associated with William James, John Dewey, and F.C.S.Schiller, according to which truth of a belief is identified by its usefulness. “A propositioncounts as true if and only if behavior based on a belief in the proposition leads, in the longrun and all things considered, to beneficial results for the believers” (Kirkham 1998b).

The main criticisms of the pragmatic theory of truth are related to ambiguity and difficultyin understanding the truth, a point such that, in Pojman’s opinion, these criteria may leadto a form of relativist theory of truth. On the other hand, reaching consensus as a criterionfor truth cannot be convincing, since there are many examples that are true at the time ofproposing but there is no agreement on them.

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Table 2 Definitions of truth theories

Truth theory Definition

Correspondence Truth consists in its agreement with (or correspondence to) reality

Coherence The most we can say by way of a general definition is that a set of two ormore beliefs are said to cohere if they ‘fit’ together or ‘agree’ with oneanother

Pragmatic 1. True proposition is one that would be endorsed unanimously by allpersons who had had sufficient relevant experiences to judge it

2. Truth of a belief is identified by its usefulness

Relativist Truth “always really means “true-by-the-standards-of-X,” where X is someindividual or group”

5.4 The Relativist Theory of Truth

This theory seems to be new, but it originated in Protagoras’ (c.490-c.420 BC) famousformula: “Man is the measure of all things, of things that are, that they are, and of thingsthat are not, that they are not.”23 There are many interpretations of this statement but, in onerelated to relativist theory of truth, it is said that personal view is the criterion of falsehoodor truth of a proposition.

In other words, according to relativist doctrine, truth “always really means “true-by-the-standards-of-X,” where X is some individual or group.” “Such a theory allows that truth maydiffer from group to group or from person to person, since social conditions and individualpsychology may affect the satisfaction to be had from a given belief” (Craig 1998).

Proponents of this kind of theory face various problems. Lack of conceptual scheme,paradox in some statements, and anarchism (which is the result of this theory) are just someof important objections to the relativist theory of truth (Harre and Krausz 1996: pp. 96–94)(Table 2).

5.5 Truth Theories in Validating System Dynamics Models

As it is mentioned at the start of this discussion, truth theories are related to the validationphase of SD modeling. All the philosophies which authors indicated about SD models havesome key points for the validation phase. For example, when Barlas and Carpenter addresseda “relativist philosophy of science” for SD models, their idea is that knowledge is confirmedand accepted through social interaction and it is relative to a context and so on.

However, our idea is a straight-forward one because we count all possible theories and theone, which is remained, is comparing and analyzing them according to SD models. Beforeentering the discussion, it is necessary to note that truth-testing in SD models is proceduraland, like quality assurance, is gradual and reciprocal from the first to the last stages of themodeling process or even after that. This issue makes truth-testing a dynamic process towardgradually attracting confidence and validation between modelers and clients. The dynamicprocess of truth making as an outstanding property of SD modeling could be explained in

23 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 collectionof the ancient sources both fragments and testimonia, the latter designated with “A” , includes Greek texts ofthe fragments with translations in German.) Routledge Encyclopedia of Philosophy, 1998.

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term of the Bayesian epistemology24 (such as conditionalization principle). In this regard,knowledge refinement after entering new information in a causal Bayesian learning networkwill be similar to the iterative process of constructing dynamic hypothesis for a complexproblem by “beliefs in variables” changes25.

However, by looking at SD models and the quotes of the pioneers of the field (despiteof lack of consensus in the issue), it could be said that the nature of truth in SD modelsis correspondence theory.26 Based on this theory, all variables of models can and shouldhave quantitative and conceptual correspondence in the real system. However, the claim ofcorrespondence theory regarding the nature of truth in applied situations reveals the existingdifficulties in this theory, especially in socio-economic fields; therefore, system dynamicistsemphasized other theories as a criterion of truth. The pragmatism, relativism and coherencetheories (and even correspondence theory) are the most indicated theories that are countedas truth theory in SD.

Considering SD’s justification structure (coherentism), and the pivotal role of mentalmodels in the modeling process, it seems that the coherency can be the most suitable standardin the validation of SD models. Thus, for SD, the truth has collective not individual essence,so that it will be earned through the inter-subjective space and challenges among mentalmodels.

As such, two doctrines of internal relations and degrees of truth are dominant in SDmodels. First, each of these beliefs (in variables) should have necessary coexistence withother “beliefs (in variables)” and, as a part, have consistency with the total network or causalsystems (the doctrine of internal relations). Second, attributing truth (validation) to the modelsis not a complete truth, but it is contextually true. It is clear that by accepting this theory, theproblem of preference among alternative models remains. This is because there may be twoor more different causal systems for one problem,27 which have equal internal relation orintersubjectivity agreement, but refer to different degrees of truth. Under these circumstances,we can set criteria (such as the degree of goal attainment) as a preference criterion of modelsor belief networks (systems) to reduce this problem. It is proponent to the statement ofForrester (1961: p. 129) that said: “knowledge of all forms can be brought to bear on formingan opinion of whether or not a model is suitable to its particular purpose”.

However, as it is mentioned in justification structures, this type of justification has uniquemerits (such as holism, two sided relations, etc.); nevertheless it always suffers from the lack

24 “Traditional epistemology, with its focus on the analysis of knowledge, is relatively silent about the ques-tions of belief dynamics. If there is talk about belief change, it is generally assumed that it takes place on thebasis of learned evidence that is certain. Traditional epistemology shares this assumption with logical theoriesof 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 casesof learning and belief change, philosophers as well as researchers in artificial intelligence have formulatednew updating rules (such as Jeffrey conditionalization) and developed powerful tools such as the theory ofBayesian networks (Neapolitan 2003)”. (Dancy et al. 2010: pp. 101–102).25 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 (whenwe discover that the reasons for some of our beliefs are invalid) that is usual in modifying the primary hypothesisabout a complex problem in SD.26 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 andmeaning”.27 There is no privileged single model for a complex behavior and we could discover structures, which aregenerating the same behavior with no primary evidence for prefering among them (Olaya 2009: 9067).

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Table 3 The essence and the criterion theory of truth in SD models

The essence of truth Correspondence theory

The criterion of truth Coherencya in addition to the level of model’s goal attainment that couldinclude efficiency and usefulness of the model

a Coherency, here, is a criterion for finding correspondence theory. In other words, correspondence is anobjective description that requires an epistemological criterion, and coherency as an epistemological indexcould reveal correspondence

of a stable basis or criteria for reasoning. Therefore, a strong and flexible basis should beestablished in order to be effective in system’s truth and preference.

Based on aforementioned explanations about truth theories, in the following, the feasibil-ities of other theories as a validation theory in SD are discussed. Pragmatism theory cannotbe the correct one, due to the abstract focus on results and the lack of commitment in theSD’s justification structure. However, efficiency and usefulness can be counted as modelinggoals besides the criteria of coherence theory. Relativism theory, because of lack of coherentconceptual scheme and anarchism, cannot be very useful for SD, and using this theory maylead SD to the mentioned outcomes of relativism thought. Finally, Correspondence theory,also, does not have sufficient criteria in identifying truth, since there are difficulties in adopt-ing variables with their counterparts in reality as Forrester (1971: pp. 3–4) also emphasizedthat “we can never prove that any model is an exact representation of ‘reality”’.

All in all, we can conclude that the essence of truth in SD is correspondence theory, whilethe criterion of truth is coherency in addition to the level of model’s goal attainment that couldinclude efficiency and usefulness of the model. Of course, according to these discussions,the goal should be set on attaining the Correspondence concept and improving the degree oftruth. Thus, validation includes Correspondence theory (even partially) between real systemcomponents and belief systems (Table 3).

6 Conclusion

The aim of this paper was to explore the essence of knowledge and the knowledge productionmechanism of SD models. In other words, in this article, we have tried to translate the princi-ples of the field in terms of epistemological expressions and concepts; subsequently we haverevealed the analysis’s implications, limitations and suggested ways for reducing possiblecriticisms. However, this kind of research may be considered a new kind of philosophicalexploration of SD since it started from the structure of models, which is the center of thedebates in the field and then by concepts such as truth extended the border of discussion.

What we have argued here is that there are strange similarities between justification struc-tures and SD models that will be followed by some implications. Maybe the most importantof them is that we cannot easily prefer among SD models (a strong problem in validationdebates); however, in this paper we have suggested some ways for decreasing this inevitablestructure’s effect.

However, our claim is not that we have detailed all the facets of this conformity completely;we have merely scratched the surface of theory of knowledge in SD models, and more workremains to be done about these concepts, especially where their practical implication is anissue. On the other hand, exploring the peculiarities of SD in contrast with other ways ofobtaining knowledge could be one of these niches, which remains for interested researchers.

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All told, we hope that this paper can help to reinvigorate the advocates of methodologicalissues of SD by opening new and firm arenas for more discussions.

Acknowledgements The authors would like to thank Mohammadmahdi Soleimanipour and MohsenFeyzbakhsh for their support and ideas in revising our article. We also thank the reviewers of Foundationsof Science for their useful comments and suggestions that help us to clarify and enrich our ideas better. Wealso appreciate Professor Aerts, Editor-in-Chief of the journal for his efforts in the process of submission toacceptance of the article.

References

Alston, W. P. (1976). Two types of foundationalism. The Journal of Philosophy, 73(7), 165–185.Audi, R. (1993). The structure of justification. Cambridge: Cambridge University Press.Barlas, Y. (1996). Formal aspects of model validity and validation in system dynamics. System Dynamics

Review, 12(3), 183–210.Barlas, Y., & Carpenter, S. (1990). Philosophical roots of model validation : Two paradigms. System Dynamics

Review, 6(2), 148–166.Bell, J. A., & Bell, J. F. (1980). System dynamics and scientific method. In J. Randers (Ed.), Elements of the

system dynamics method (pp. 3–21). Cambridge, MA: Productivity Press.Bell, J. A., & Senge, P. M. (1980). Methods for enhancing refutability in system dynamics modeling. TlMS

Studies in the Management Sciences, 14, 61–73.BonJour, L. (1985). The structure of empirical knowledge. Cambridge, MA: Harvard University Press.Clune, A. C. (1997). Justification of empirical belief: Problems with Haack’s foundherentism. Philosophy,

72(281), 460–463.Craig, E. (Ed.). (1998). Relativism. In Routledge Encyclopedia of Philosophy (Version 1.0). London:

Routledge.Dancy, J., Sosa, E., et al. (2010). A companion to epistemology. Oxford: Wiley-Blackwell.DeRose, K. (1992). Contextualism and knowledge attributions. Philosophy and Phenomenological Research,

52, 913–929.DeRose, K. (1999). Contextualism: An explanation and defense. In J. Greco & E. Sosa (Eds.), The Blackwell

guide to epistemology (pp. 187–205). Oxford: Basil Blackwell.Doyle, J. K., & Ford, D. N. (1998). Mental models concepts for system dynamics research. System Dynamics

Review, 14(1), 3–29.Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: MIT Press.Forrester, J. W. (1971). Principles of systems. Cambridge: Wright-Allen Press.Forrester, J. W. (1978). Market growth as influenced by capital investment. In E. B. Roberts (Ed.), Managerial

applications of system dynamics. Cambridge, MA: Productivity Press.Gardenfors, P., & Rott, H. (1995). Belief revision. In D. M. Gabbay, C. J. Hogger, & J. A. Robinson (Eds.),

Handbook of logic in artificial intelligence and logic programming, Vol. IV: Epistemic and temporal rea-soning (p. 35132). Oxford: Oxford University Press.

Gettier, E. L. (1963). Is justified true belief knowledge? Analysis, 23, 121–123.Goldman, A. I. (1986). Epistemology and cognition. Cambridge, MA: Harvard University Press.Green, J. (1987). Dictionary of jargon. London: Routledge and Kegan Paul.Haack, S. (1993). Evidence and inquiry. Cambridge, MA and Oxford: Blackwell.Harre, R., & Krausz, M. (1996). Varieties of relativism. Oxford, UK: Oxford University Press.Kirkham, R. (1998a). Coherence theory of truth. In E. Craig (Ed.), Routledge encyclopedia of philosophy

(Version 1.0). London: Routledge.Kirkham, R. (1998b). Pragmatic theory of truth. In E. Craig (Ed.), Routledge encyclopedia of philosophy

(Version 1.0). London: Routledge.Klein, P. D. (1998). Epistemology. In E. Craig (Ed.), Routledge encyclopedia of philosophy (p. 2005). London:

Routledge.Kornblith, H. (1999). In defense of a naturalized epistemology. In J. Greco & E. Sosa (Eds.), the blackwell

guide to epistemology. Oxford: Blackwell Publishers Ltd.Lane, D. (1999). Social theory and system dynamics practice. European Journal of Operational Research,

113(3), 501–527.Luna-Reyes, L. F., & Andersen, D. L. (2003). Collecting and analyzing qualitative data for system dynamics:

Methods and models. System Dynamics Review, 19(4), 271–296.

123


Maffie, J. (1990). Recent work on naturalized epistemology. American Philosophical Quarterly, 27(4), 281–293.

Morecroft, J. D. (2007). Strategic modelling and business dynamics: A feedback systems approach. WestSussex, UK: John Wiley & Sons Ltd.

Moser, P. K., & Vander Nat, A. (Eds.). (2002). Human knowledge: Classical and contemporary approaches.Oxford: OxfordUniversity Press.

Neapolitan, R. (2003). Learning Bayesian Networks. Upper Saddle River, NJ: Prentice Hall.Olaya, C. (2009). System dynamics philosophical background and underpinnings. In R. A. Meyers & P. Kokol

(Eds.), Encyclopedia of complexity and systems science (pp. 9057–9087). New York: Springer.Pojman, L. P. (2001). What can we know? An introduction to the theory of knowledge. Florence, KY: Wadsworth

Publishing/Thomson Learning.Pruyt, E. (2006). What is system dynamics? A paradigmatic inquiry. In Proceedings of the 24th interna-

tional conference of the system dynamics society, (pp. 102–103). Nijmegen, The Netherlands: The SystemDynamics Society.

Quine, W. V. (1969). Epistemology naturalized. In W. V. Quine (Ed.), Ontological relativity and other essays.New York: Columbia University Press.

Quinn, P. L. (2002). Epistemology in philosophy of religion. In P. K. Moser (Ed.), The oxford handbookof epistemology (pp. 513–538). Oxford: Oxford University Press.

Rescher, N. (1973). The coherence theory of truth. Oxford: Oxford University Press.Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. Boston:

Irwin/McGraw-Hill.Vazquez, M., & Liz, M. (2007). System dynamics and philosophy: A constructivist and expressivist approach.

In Proceedings of the international conference of the system dynamics society, Boston, MA: The SystemDynamics Society.

Vazquez, M., Liz, M., & Aracil, J. (1996). Knowledge and reality: Some conceptual issues in system dynamicsmodeling. System Dynamics Review, 12(1), 21–37.

Author Biographies

Mohammadreza Zolfagharian earned his continuous master degree in Islamic Studies and Industrial Man-agement from Imam Sadiq University (Tehran), where he is now working as researcher at faculty of IslamicStudies and Management. His M.A thesis topic was: “Explanation of System Dynamics Philosophy and ItsImplications in Management: Using Inductive and Deductive Methods” that was defended on September2010. This article is one of the papers, which are elicited from his M.A thesis. His main research and studyinterests focused on systems thinking and systemic practice, and methodological issues of problem structur-ing methods (System Dynamics among others) in an interdisciplinary context (philosophy, social science,and psychology), as well as, using them practically for complex and unstructured problems articulation andpolicy analysis. He was, also, a member of research projects in areas of qualitative research, educational sys-tems, strategic planning, human resource management, and business ethics, and has published some resultsof his research in Persian journals and publications.

Reza Akbari completed his PhD in Islamic Philosophy and Kalam at University of Tehran, and now is asso-ciate professor at Imam Sadiq University in Islamic Republic of Iran. He was visiting professor at TarbiatModares University, University of Tehran and Allameh Tabatabai University. He has been the head of ImamSadiq Interdisciplinary Research Center for five years. He has a wide variety of academic interests, includ-ing Islamic philosophy, Epistemology, philosophy of religion, philosophy of mind, ethics and formal logic.He is the editor of “Pazhuheshnameh falsafeh Din” (philosophy of religion research), an academic journalin Islamic Republic of Iran devoted to philosophical problems in the realm of theology and philosophy ofreligion, and on the editorial boards of several philosophical journals in Islamic Republic of Iran. He is theauthor of Fideism and Immortality, two famous books in philosophical circles in Islamic Republic of Iran,and many philosophical papers.

Hamidreza Fartookzadeh completed his PhD in systems management from Tehran University, and nowis associate professor at Malek Ashtar University of Technology (Tehran). He taught courses in SystemDynamics and Systems Thinking at graduate and PhD levels at Tarbiat Modares University, University ofTehran, Allameh Tabatabai University and Imam Sadiq University. He has delivered presentations in a vari-ety of national conferences and workshops for academics and practitioners. He is the author of many papersin Persian journals and publications, as he is among the editorial boards of management journals in IslamicRepublic of Iran. Currently, his main research interests are analyzing dynamics of socio-economic and tech-nical systems.

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theory of knowledge in system dynamics models

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