The purpose of this paper is to present a possible way in which a marriage between Artificial Intelligence and Modeling can take place. More specifically it is the purpose of the paper to explore some basic concepts related to Artificial Intelligence and by using an expert system shell called GURU to aid in the development of system dynamics models. The concept is one of going from data base to knowledge base to models and to examine the line of reasoning that is used in formulating the problem. Simple examples will explore the potential of this approach.
In this poster we analyse the chaotic motion of a model which describes the behavior of a prey-predator-food system.This system can be modeled by mixing two well known models: the predator-prey model (Henize, 1971) and the Kaibab plateau model, which copes with the prey-food part of the model (Godman, 1974).This model has previously been introduced in (Toro and Aracil, 1988).
The continuous model for a deterministic continuous growth, one-predator-one-prey system is that of Lotka and Volterra. It is well known that this model predicts neutral stability in which the constant amplitudes of the oscillations are determined by the initial conditions. Without changing the underlying model assumptions and by altering only the predator functional response to prey density, it is shown that damped oscillations towards stable equilibrium or explosive oscillations or a stable limit cycle can be generated as model input.
A bifurcation sequence in the Waycross model is studied by means of Poincaré section techniques. The bifurcation parameter B is gradually reduced from 2.00 to 1.50. This parameter measures the inclination of one type of minority families (Lomanians) to move into the districts with many families of another type of minority population (Itrachians). Because of symmetry the attractors in this 4-dimensional migratory model occur in pairs with opposite directions of cyclic population movements. A pair of simple limit cycle attractors are found to remain stable under formation of a pair of period-2 attractors. In a certain parameter range, the model thus contains four entangled attractors. We follow how the period-2 attractor become chaotic through formation and subsequent destabilization of 2-dimensional tori. On the way, regular period-14, period-18 and period-4 attractors are produced through frequency-locking. We thereafter observe a case of type-III intermittency when the two period-1 orbits become unstable, and finally the two chaotic attractors merge with each other.
Studies in the psychology of individual choice have identified numerous cognitive, informational,temporal, and other limitations which bound human rationality, often producing systematic errors and biases in judgments and choice. Yet for the most part models of aggregate phenomena in management science and economics have not adopted postulates of human behavior consistent with such micro-empirical knowledge of individual decisionmaking. One reason has been the difficulty of extending the experimental methods used to study individual decisions to aggregate, dynamic settings. This paper reports an experiment on the generation of macro-dynamics from microstructure in a common and important managerial context. Subjects play the role of managers in a simulated inventory management system, the “Beer Distribution Game”. The simulated environment contains multiple actors, feedbacks, nonlinearities, and time delays. The interaction of individual decisions with the structure of the simulated firm produces aggregate dynamics which systematically diverge from optimal behavior. Subjects generate large amplitude oscillations with stable phase and gain relationships among the variables. An anchoring and adjustment heuristic for stock management is proposed as a model of the subject's decision process. The parameters of the rule are estimated and the rule is shown to explain the subjects’ behavior well. Analysis shows the subjects fall victim to several 'misperceptions of feedback' identified in prior experimental studies of dynamic decisionmaking. Specifically, they fail to account for control actions which have been initiated but have not yet had their effect. More subtle, subjects are insensitive to the presence of feedback from their decisions to the environment and attribute the dynamics to exogenous variables, leading their normative efforts away from the source of difficulty. The experimental results are related to prior tests of the proposed heuristic and the generality of the results is considered. Finally the implications for behavioral theories of aggregate social and economic dynamics are explored.
This paper is attempting at modeling and simulation of an educational problem at junior and senior high schools. Our model consists of seven level-variables, ten rate- variables and twenty auxiliary -variables. Also we discuss marks of students in the model that are figured from 0 to 100. Results of the computer simulation are given to illustrate the our model.
While the informal modelling procedure of system dynamics qualifies as scientific according to the definitions of the epistemological literature, the application of this procedure may create models of phenomena that provide few clues to the design of change. Policy design exercises based on such models may often end with a moral statement about what should be done by the organization as a whole instead of providing motivational instruments through which its various members realize evolutionary change. Unfortunately, a change prescribed by a moral statement can only be realized by a powerful intervention by an outside agent which is, if at all possible to implement, often dysfunctional. This paper attempts to define heuristics for the construction of models that may lead to viable designs of evolutionary change. A model is viewed as instrument for understanding a problem not as a source of design. Guidelines for partitioning complex problems into multiple models are discussed. Models containing conservative systems capable of generating a large number of time variant patterns, which are in reality separated by time and location, appear to be sound instruments for facilitating the design of change.
The “escalation phenomenon” (Staw 1976; Staw and Ross 1978) refers to the tendency for decision makers to “throw good money after bad,” that is, to invest beyond the point where benefits equal costs. The commonly accepted view is that such 'escalation” occurs as a result of decision makers becoming overcommitted to a previously chosen course of action through a series of decision errors. This paper presents a generic system dynamics model of resource recommitment behavior that is able to produce “escalation” without the presence of decision error. Implications of this model to the theory and practice of project management are discussed.
In the past ten years, system dynamics has become more accessible to managers and more applicable to strategic issues. The paper reviews developments in software, theory, gaming and methods of simulation analysis that have brought about this change. Together these developments allow modellers to create computer-based learning environments (or microworlds) for managers to “play-with” their knowledge of business and social systems and to debate strategic change.
Intelligent behavior involves subjective variables, it is guided by fuzzy goals and constraints, and it applies multi-valued rules of inference to reach its conclusion. Decision or strategy support systems- in order to serve as reliable tools for testing the consequences of alternative courses of action- must reflect these essential aspects of the problem under investigation.The paper presents a corporate model designed for the investigation of a firm's resource allocation strategy. It discusses the applicability of fuzzy set theory to computer simulation in general and to System Dynamics in particular. After qualitative variables and fuzzy goals have been explicitly included, the model exhibits improved performance with respect to behavior and acceptance by management.