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3. Models of Mind




  Various models have been proposed of the human and animal mind The unified efforts of those working on understanding the mind has been dubbed "cognitive science"
  The models vary by their purpose  
  Psychology / sociology Understanding humans and animals in toto
  Engineering psychology Models are built to predict performance characteristics of humans in various circumstances
  Cognitive science How does the human and animal minds work? Empirical, computer science and model emphasis.
  Artificial intelligence "Let's make machines intelligent."











Coarse-Grain Mind Model of Intelligent Systems
  Sensation What comes in through the senses - "raw information" (processed data)
  Perception Includes interpretation and decisions about the sensory data
  Interpretation Interpret the perceptual data in context with knowledge and experience
  Decision Interpretation, decision making and planning are often intermixed. A simplification includes an initial decision between interpretation and planning, the decision to act.
  Planning The (mental and physical) act of deciding future actions
  Action The execution of a decision/plan
  Goals It is generally considered necesssary to model all cognitive agents as having goals; without goals comparisons between choices cannot be made and decisions become random.
  Memory Memory is "everywhere" in a mind. Various memories serve various purposes. Nobody knows how the human memory works as a whole. The most important ones for us are Long-Term memory and Short-Term/Working memory.
  TAKEN ABSTRACTLY All these concepts can be applied to firms, institutions, organizations and even countries!
















Perception of Environmental Change
  Perception of the mean Requires collecting data over time.
  Perception of proportions Requires comparing options of different sizes. Fairly important.
  Perception of growth Requires extrapolation and prediction. Very important when modeling corporations but less so for individuals.
  Temporal sequencing

A always follows B; whenever B occurs, C and D will follow, in that order.

Single A->B sequencing can be useful in business ("If I raise my price in condtion X, I will sell more").

  Estimating variability

A always changes more than B under the influence of C, except under circumstances D.


  Correlational structure

Correlation between A and B: whenever A changes, B changes too.

There is no correlation without causation - something is causing the correlation (obviously). (However, the causation can be complex and not betwen A and B.)

Correlation does not imply causation. (B does not have to cause A and A does not have to cause B.)

Rather complex to implement but is important for mind models if the agents are to make predictions.

  Causal structure

One can uncover causal chains (A affects B affects C, which explains why C is like C is) by observing repeated behavioral patterns of events.

It is generally considered rather complex to uncover causal structures through perception and knowledge.

  Statistical inferencing

Making an inference based on statistics.

Could be useful as an approximation to causal structure. Necessary for modeling correlational structure.














Important Issues for Individuals
  Risk-taking Individuals take more risks when they owe money than when they own money.
  Perception of growth Both individuals and groups are more conservative than they should be in light of exponential curves.
  Limited number of hypotheses Only 4-5 hypotheses at a time. Important for learning.
  Negative evidence/hypotheses A bias towards being ignored.











Architecture: Coordination hierarchies
  Models of mind (and markets) It's all a question of architecture
  Questions to ask when creating architectures
  • What is the data?
  • Where is the data?
  • How is it shared?
  • How is it processed/changed?
    Coordination hierachies: The human and animal minds are probably ... ?