Posts Tagged: representation

representational issues, framing

Chapter: The functional task environment


Human beings, viewed as a behaving system, are quite simple.
The apparent complexity of our behavior over time is largely a reflection
of the complexity of the environment in which we find ourselves.
(Simon, 1996, p. 53)

Wayne D. Gray, Hansjörg Neth, Michael J. Schoelles

The functional task environment

From the introduction:  Although human thought may be possible in those floatation tanks that are used to encourage meditative states, in by far the majority of instances thought occurs in the context of some physical task environment. The physical environment can be as simple as a light and book. It can be as complex as the face of a mountain and the equipment of the climber. It may be as dynamic as the cockpit of an F-16 in supersonic flight and as reactive as a firefight in Iraq or as heated as an argument between lovers.

Paper: Melioration dominates maximization

There is no reason to suppose that most human beings are
engaged in maximizing anything unless it be unhappiness,
and even this with incomplete success.
R.H. Coase (1980), The Firm, the Market, and the Law, p. 4

[Copyright neth.de, 2006]:

Hans Neth, Chris Sims, Wayne Gray (2006). Melioration dominates maximization: Stable suboptimal performance despite global feedback. Paper presented at CogSci 2006.

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

Melioration dominates maximization: Stable suboptimal performance despite global feedback

Abstract:  Situations that present individuals with a conflict between local and global gains often evoke a behavioral pattern known as melioration — a preference for immediate rewards over higher long-term gains.  Using a variant of a binary forced- choice paradigm by Tunney & Shanks (2002), we explored the potential role of global feedback as a means to reduce this bias. 

Paper: Melioration despite informative feedback

In many ways the word ‘meliorizing’ expresses a sensible middle way between optimizing and satisficing.  Where optimus means best, melior means better. (…)
Like a river, natural selection blindly meliorizes its way down successive lines of immediately available least resistance. The animal that results is not the most perfect design conceivable, nor is it merely good enough to scrape by.  It is the product of a historical sequence of changes, each one of which represented, at best, the better of the alternatives that happened to be around at the time.
Richard Dawkins (1982), The Extended Phenotype: The Long Reach of the Gene, p. 46

[Copyright neth.de, 2005–2014]:

Hans Neth, Chris Sims, Wayne Gray (2005).

Melioration despite more information: The role of feedback frequency in stable suboptimal performance.

Paper presented at HFES 2005.

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

Melioration despite more information: The role of feedback frequency in stable suboptimal performance

Abstract:  Situations that present individuals with a conflict between local and global gains often result in a behavioral pattern known as melioration — a preference for immediate rewards over higher long-term gains.  Using a variant of a paradigm by Tunney & Shanks (2002), we explored the potential role of feedback as a means to reduce this bias.  We hypothesized that frequent and informative feedback about optimal performance might be the key to enable people to overcome the documented tendency to meliorate when choices are rewarded probabilistically.  Much to our surprise, this intuition turned out to be mistaken. Instead of maximizing, 19 out of 22 participants demonstrated a clear bias towards melioration, regardless of feedback condition.  From a human factors perspective, our results suggest that even frequent normative feedback may be insufficient to overcome inefficient choice allocation. We discuss implications for the theoretical notion of rationality and provide suggestions for future research that might promote melioration as an explanatory mechanism in applied contexts.

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.