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|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.|
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").
A always changes more than B under the influence of C, except under circumstances D.
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.
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.
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||
|Coordination hierachies: The human and animal minds are probably ... ?|