Mental Models of Dynamic Systems

A real dynamical system is any concrete object which changes over time. A mathematical dynamical system, on the other hand, is an abstract mathematical structure which can be used to describe the change of a real system . as an evolution through a series of states . ( Thus , only real dynamical systems actually undergo change ; mathematical dynamical systems are timeless , unchanging entities which can nevertheless be used as models of change in real systems . ) If the evolution of the real system is detenninistic , i.e. , if the state at any future time is determined by the state at the present time, then the abstract mathematical structure consists of three elements . The first element is Real VB . Mathematical

Studies in Systems, Decision and Control, 2022

The series "Studies in Systems, Decision and Control" (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control-quickly, up to date and with a high quality. The intent is to cover the theory, applications, and perspectives on the state of the art and future developments relevant to systems, decision making, control, complex processes and related areas, as embedded in the fields of engineering, computer science, physics, economics, social and life sciences, as well as the paradigms and methodologies behind them. The series contains monographs, textbooks, lecture notes and edited volumes in systems, decision making and control spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution and exposure which enable both a wide and rapid dissemination of research output.

Springer eBooks, 2007

As scientists who are interested in studying people's mental models, we must develop appropriate experimental methods and discard our hopes of finding neat, elegant mental models, but instead learn to understand the messy, sloppy, incomplete, and indistinct structures that people actually have. Donald A. Norman [1983, p. 14]

Much of human decision making occurs in dynamic situations where decision makers have to control a number of interrelated elements (dynamic systems control). Although in recent years progress has been made toward assessing individual differences in control performance, the cognitive processes underlying exploration and control of dynamic systems are not yet well understood. In this perspectives article we examine the contribution of different approaches to modeling cognition in dynamic systems control, including instance-based learning, heuristic models, complex knowledge-based models and models of causal learning. We conclude that each approach has particular strengths in modeling certain aspects of cognition in dynamic systems control. In particular, Bayesian models of causal learning and hybrid models combining heuristic strategies with reinforcement learning appear to be promising avenues for further work in this field.

1994

Ongoing research at the Rockefeller College is exploring the ability of subjects in a computerbased management laboratory to manage the implementation of welfareseform. Reflections on the design of such research have pushed us to develop a firmer theoretical foundation to guide our research on mental models in dynamic decision making. We posit that mental models are multifaceted, including distinguishable submodels focused on ends (goals), means (strategies, tactics, policy levers) and connections between them (the means/ends model). These distinctions, coupled with a view of human judgment from Brunswikean psychology, lead to a rich integrated theory of perception, planning. action, and learning in complex dynamic feedback systems. From that theory we derive classes of testable research hypotheses about decision making in dynamic environments- in particular, ”design logic ” and “operator logic ” hypotheses- that have serious implications for system dynamics research and practice. T...

2012

One of the main goals of system dynamics models is to improve decision making in dynamic systems. This paper addresses the question of how we can measure what people understand about dynamic systems and what benefit people get from exposure to system dynamics models. For this purpose, we use existing literature about assessing understanding and learning in system dynamics to reflect on outstanding research questions in this area. Learning about dynamic systems requires restructuring of existing knowledge into new knowledge as well as reuse of such new knowledge over time and in different contexts. Existing approaches in system dynamics use elements of dynamic systems to represent knowledge. They thus provide a benchmark for expert knowledge and give indications about the gap between novices and experts. However, they do not provide a theory for further investigating how this gap can be closed. In a second part, we therefore analyze the learning sciences literature for elements that ...

1994

Ongoing research at the Rockefeller College is exploring the ability of subjects in a computerbased management laboratory to manage the implementation of welfareseform. Reflections on the design of such research have pushed us to develop a firmer theoretical foundation to guide our research on mental models in dynamic decision making. We posit that mental models are multifaceted, including distinguishable submodels focused on ends (goals), means (strategies, tactics, policy levers) and connections between them (the means/ends model). These distinctions, coupled with a view of human judgment from Brunswikean psychology, lead to a rich integrated theory of perception, planning. action, and learning in complex dynamic feedback systems. From that theory we derive classes of testable research hypotheses about decision making in dynamic environments in particular, ”design logic” and “operator logic” hypotheses that have serious implications for system dynamics research and practice. The o...

2006

The performance of any computer-based system (see Chapter 1 for a definition) is the result of an interaction between the humans, the technology, and the environment, including the physical and organisational context within which the system is located. Each of these high level components (human, technology and environment) has its own structure. In addition to the static features of some components (such as the human's physiology, the architecture of the technology, etc.), the dynamic structure also has to be considered.

Brain and Cognition, 2003

Encyclopedia of Cognitive Science, 2006

The dynamical hypothesis in cognition identifies various research paradigms applying the mathematics of dynamical systems to understanding cognitive function. The approach is allied with and partly inspired by research in neural science over the past fifty years for which dynamical equations have been found to provide excellent models for the behavior of single neurons (Hodgkins and Huxley, 1952). It also derives inspiration from work on gross motor activity by the limbs (e.g., Bernstein, 1967. In the early 1950s, Ashby made the startling proposal that all of cognition might be accounted for with dynamical system models (1952), but little work directly followed from his speculation due to a lack of appropriate mathematical methods and computational tools to implement practical models. More recently, the connectionist movement (Rumelhart and McClelland, 1986) provided insights and mathematical implementations of perception and learning, for example, that have helped restore interest in dynamical modeling.