An important point to make, but what of the optimal meta-strategy (strategy in forming strategies)?
I recognise the enormous advantage that a formal (reasoned) analysis of a problem provides however is this strategy statistically optimal (i.e. likely to lead to a win) in most environments?
For example, most challenges are time limited, so extensive analysis is impractical. In addition, the problem (and the solution) may not lend itself to rational analysis but instead require internal mental statistical modelling (e.g. how should I throw a rock in order to hit a target may be best answered by repeatedly trying).
In the example in the article the assumption that the deck of cards is random may itself be unreasonable (and when averaged over many challenges may be a sub-optimal heuristic). The strategy employed by those playing may appear random but may in fact represent an (information theoretically) optimal hypothesis of the likely next result given the previous inputs exploiting a set of modelling heuristics that are themselves optimal selections given the past experience and genetic history. This is likely to produce an output that had a matching distribution (because in a situation where a correct model could be produced it would have this distribution).
The argument that some problems ‘are not rational’ may actually be an indication that the problem solving strategy of reasoned analysis has not produced positive results in their experience and so they are accurately communicating their statistical meta-knowledge. For them to alter their strategy in an optimal way would require that they had a statistically valid reason for doing so, i.e. that they were aware that such approaches had led to superior results in the past. Of course they have no means of communicating this way because their experiences have not led them to develop the conscious models that would enable that kind of self awareness.
An important point to make, but what of the optimal meta-strategy (strategy in forming strategies)?
I recognise the enormous advantage that a formal (reasoned) analysis of a problem provides however is this strategy statistically optimal (i.e. likely to lead to a win) in most environments?
For example, most challenges are time limited, so extensive analysis is impractical. In addition, the problem (and the solution) may not lend itself to rational analysis but instead require internal mental statistical modelling (e.g. how should I throw a rock in order to hit a target may be best answered by repeatedly trying).
In the example in the article the assumption that the deck of cards is random may itself be unreasonable (and when averaged over many challenges may be a sub-optimal heuristic). The strategy employed by those playing may appear random but may in fact represent an (information theoretically) optimal hypothesis of the likely next result given the previous inputs exploiting a set of modelling heuristics that are themselves optimal selections given the past experience and genetic history. This is likely to produce an output that had a matching distribution (because in a situation where a correct model could be produced it would have this distribution). The argument that some problems ‘are not rational’ may actually be an indication that the problem solving strategy of reasoned analysis has not produced positive results in their experience and so they are accurately communicating their statistical meta-knowledge. For them to alter their strategy in an optimal way would require that they had a statistically valid reason for doing so, i.e. that they were aware that such approaches had led to superior results in the past. Of course they have no means of communicating this way because their experiences have not led them to develop the conscious models that would enable that kind of self awareness.