Posts in Category: simulation

computational simulation

Paper: Arithmetic with Arabic vs. Roman numerals

… how information is represented can greatly affect how easy it is
to do different things with it. (…) it is easy to add, to subtract,
and even to multiply if the Arabic or binary representations are used,
but it is not at all easy to do these things — especially multiplication —
with Roman numerals.  This is a key reason why the Roman culture failed
to develop mathematics in the way the earlier Arabic cultures had.
D Marr (1982): Vision, p. 21

[Copyright, 2008]:

Dirk Schlimm and Hans Neth (2008).

Modeling ancient and modern arithmetic practices: Addition and multiplication with Arabic and Roman numerals. Paper presented at CogSci 2008.

Dirk Schlimm, Hansjörg Neth

Modeling ancient and modern arithmetic practices: Addition and multiplication with Arabic and Roman numerals

Abstract:  To analyze the task of mental arithmetic with external representations in different number systems we model algorithms for addition and multiplication with Arabic and Roman numerals.  This demonstrates that Roman numerals are not only informationally equivalent to Arabic ones but also computationally similar — a claim that is widely disputed.  An analysis of our models’ elementary processing steps reveals intricate trade-offs between problem representation, algorithm, and interactive resources.  Our simulations allow for a more nuanced view of the received wisdom on Roman numerals.  While symbolic computation with Roman numerals requires fewer internal resources than with Arabic ones, the large number of needed symbols inflates the number of external processing steps.

Paper: Juggling multiple tasks in a synthetic task environment

Doing two things at once, like singing while you take a shower,
is not the same as instant messaging while writing a research report.
Don’t fool yourself into thinking you can multitask jobs that need your
full attention. You’re not really having a conversation while you write;
you’re shifting your attention back and forth between the two activities quickly.
You’re juggling. When you juggle tasks, your work suffers AND takes longer
— because switching tasks costs.
Gina Trapani, Work Smart,

[Copyright, 2006]:

Hans Neth, Brittney Oppermann, Sunny Khemlani, Wayne Gray (2006)

Juggling multiple tasks: A rational analysis of multitasking in a synthetic task environment.

Paper presented at HFES 2006, San Francisco, CA, USA.

Hansjörg Neth, Sangeet S. Khemlani, Brittney Oppermann, Wayne D. Gray

Juggling multiple tasks: A rational analysis of multitasking in a synthetic task environment

Abstract:  Tardast (Shakeri 2003; Shakeri & Funk, in press) is a new and intriguing paradigm to investigate human multitasking behavior, complex system management, and supervisory control.  We present a replication and extension of the original Tardast study that assesses operators’ learning curve and explains gains in performance in terms of increased sensitivity to task parameters and a superior ability of better operators to prioritize tasks.  We then compare human performance profiles to various artificial software agents that provide benchmarks of optimal and baseline performance and illustrate the outcomes of simple heuristic strategies.  Whereas it is not surprising that human operators fail to achieve an ideal criterion of performance, we demonstrate that humans also fall short of a principally achievable standard.  Despite significant improvements with practice, Tardast operators exhibit stable sub-optimal performance in their time-to-task allocations.

Paper: Dynamic memory updates in TRACS

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

[Copyright, 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.