Posts Tagged: JDM

judgment & decision making

Paper: Dynamic memory updates in TRACS

You can’t play 20 questions with nature and win.
Allen Newell (1973)

[Copyright neth.de, 2004]: Hans Neth, Chris Sims, Dan Veksler, Wayne Gray: Dynamic memory updates in TRACS. Paper presented at CogSci 2004.


Hansjörg Neth, Chris R. Sims, Vladislav D. Veksler, Wayne D. Gray

You can’t play straight TRACS and win: Memory updates in a dynamic task environment

Abstract:  To investigate people’s ability to update memory in a dynamic task environment we use the experimental card game TRACS^tm (Burns, 2001).  In many card games card counting is a component of optimal performance. However, for TRACS, Burns (2002a) reported that players exhibited a baseline bias:  rather than basing their choices on the actual number of cards remaining in the deck, they chose cards based on the initial composition of the deck.  Both a task analysis and computer simulation show that a perfectly executed memory update strategy has minimal value in the original game, suggesting that a baseline strategy is a rational adaptation to the demands of the original game.  We then redesign the game to maximize the difference in performance between baseline and update strategies.  An empirical study with the new game shows that players perform much better than could be achieved by a baseline strategy.  Hence, we conclude that people will adopt a memory update strategy when the benefits outweigh the costs.

Paper: simBorgs modeling dynamic decision making


For the exogenously extended organizational complex
functioning as an integrated homeostatic system unconsciously,
we propose the term “cyborg”.
M.E. Clynes and N.S. Kline (1960), Cyborgs and Space (Astronautics, 13)

[Copyright neth.de, 2004–2014]:

Chris Myers, Hans Neth, Mike Schoelles, Wayne Gray (2004): The simBorg approach to modeling a dynamic decision-making task. ICCM 6, CMU, Pittsburgh, USA.

Christopher W. Myers, Hansjörg Neth, Michael J. Schoelles, Wayne D. Gray

The simBorg approach to modeling a dynamic decision-making task

Abstract:  The simulated cyborg (or, simBorg) approach blends computational embodied-cognitive models of interactive behavior with artificial intelligence based components in a simulated task environment (Gray, Schoelles, & Veksler, 2004).  simBorgs combine human and machine components. This combination of high fidelity cognitive modeling (human) and AI (machine) facilitates the development of families of models that allow the modeler to hold components (memory, vision, etc) at different levels of expertise without concern for cognitive plausibility. For example, rather than modeling human problem solving, the modeler can rely on various black-box techniques (i.e., cognitively implausible AI), thereby focusing on predicting how subtle differences in costs and benefits in interactive methods affect performance and errors. The current modeling endeavor adopts the simBorg approach in order to build a family of interactive decision-making agents.