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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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Of Models and Machines: Implementing Bounded Rationality.

Stephanie Dick

    Isis; an International Review Devoted to the History of Science and Its Cultural Influences
    |December 22, 2015
    PubMed
    Summary

    Herbert Simon's bounded rationality principle was shaped by early AI research. Implementing the Logic Theory Machine revealed how computer constraints influenced cognitive models.

    Area of Science:

    • Artificial Intelligence
    • Cognitive Science
    • History of Computing

    Background:

    • Explores Herbert Simon's bounded rationality principle within mid-1950s Artificial Intelligence research.
    • Focuses on the RAND Corporation's effort to model human reasoning as a computer program, the Logic Theory Machine.
    • Highlights the belief that computers and minds are analogous information-processing systems.

    Discussion:

    • Details the challenges in translating human logic problem-solving into the JOHNNIAC computer program.
    • Examines how material constraints and machine affordances necessitated new tools and practices.
    • Argues against a strict separation of internal cognitive practices and external tools/materials.

    Key Insights:

    • Early AI implementation revealed that computational constraints significantly shape cognitive models.

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  • The development of the Logic Theory Machine demonstrates the co-evolution of cognitive theory and technological affordances.
  • Bounded rationality is not solely an internal cognitive limit but is influenced by external computational environments.

    • Outlook:

    • Suggests a re-evaluation of cognitive models in light of technological implementation.
    • Emphasizes the reciprocal relationship between computational tools and the understanding of human cognition.
    • Provides historical context for contemporary human-computer interaction and AI research.