"I don't know which is worse... that everyone has his price, or that the price is always so low."
-- Hobbes

The capitalist framework is quite general, because as much as capitalist theories dictate when one should invest, they are completely agnostic as to what/when one should consume.

The idea of consumption is exogenous to capitalism and can be defined to be anything. For investment to take place, all that is required is a sufficiently low discount rate. For example, let's say you want to save starving children in Africa. If you value feeding 10000 starving children in a year more than you value feeding 1000 starving children now, you have a 900% discount rate and should invest in anything with greater than 900% annual return. This particular hurdle rate would almost be impossible to overcome, but most altruistic goals have lower discount rates than that.

Extra credit: does morality have a discount rate?

On Monday's Econtalk, Taleb said that knowledge was harmful. Here's my take.

Formalisms are born in academia, where they establish boundaries within which solutions are sought. Those boundaries make it possible for ideas to engage, by directing people to compete on the same problems. Boundaries are necessary for focus, they make it easier to share and hybridize ideas. The real game (of life) is broken up into sub-games which academics play in for status and respect.

The sub-games of academia have independent  intellectual histories and traditions. Within each sub-game, there is enough competition that solutions are dependable and valid. However, to actually use an academic result in the real world, one has to make sure the sub-game itself is set up correctly. Sub-game incorrectness takes longer to catch - paradigms have inertia. Assumptions are often accepted by players to make the competition a good game with clear winners, not so much to reflect reality.

Alas, the academic quantification of risk in the finance sub-game has falsely enthralled us all, when in fact it is a weak shadow of real world uncertainty.

In decision theory, risk adversity is a terminal goal

In decision theory, risk adversity is represented by concavity in the utility function. Concave functions bend downwards, so a straight line drawn between any two points would lie below the function line, and averaging any two function values by any weight would have a smaller value than the function at the averaged position.

Px*f(x)+Py*f(y) < f(Px*x+Py*y)

For a given average outcome, the expected utility is higher if there are fewer possible outcomes. Maximizing expected utility then makes the agent prefer a smaller variance in outcomes. This is how decision theory generates risk adverse behavior.

Presented in this way, the desire to reduce variance is fundamental, an ends in itself, and not subject to justification.

Risk adversity seems to be instrumental in reality

When I consider a risky choice, however, I don't undergo anything like the process of utility maximization. If I am highly uncertain, I may

1. delay the decision and acquire more information
2. avoid the choice to avoid complicating the context of future decisions
3. avoid the choice because of the fear that uncertainty makes me susceptible to fraud, since other agents have freedom in the uncertain space.

These reasons all point to uncertainty as an indicator of incomplete information, and as a cue to spend more on information or a chance to avoid future information cost, something a priori excluded if you assume information costs to be zero, as does canonical economic theory.

Presented in this way, the desire to reduce uncertainty is instrumental, a means of bettering the expected outcome, not a goal in itself.

Conclusion

Risk adversity's relationship to the management of ignorance cannot be denied. Characterizing risk adversity as the maximization of concave utility functions misses this point. Exogeny is useful for compartmentalizing and communicating theories, but in making ignorance exogenous you restrict an agent from choosing to learn more or to avoid the cost of future learning. In the presence of this restriction, the model will not display a realistic reaction to risk.

Related

• http://en.wikipedia.org/wiki/Search_theory