Utilities [aka 'decision values'] are real numbers ranging from 0 to 1,000 that take action potentials per second as their natural units.
Does this mean there is a limit on how much we can want/prefer something?
Yes, though keep in mind that these utilities are renormalized, so the important metric is how much more one option is valued over the others during a particular choice, not the absolute value of how many action potentials per second a particular option is "valued" with. The value for the exact same thing (say, an apple) could be represented by a different number of action potentials per second depending on the other options in the choice set, among other factors (e.g. context effects).
This does, it seems to me, give some neurobiological credence to a particular solution to Pascal's Mugging: bounding your utility function.
What is it for something to give neurobiological credence to the "bounded utility" answer to Pascal's Mugging?
Do they have this down to particular physical neural pathways, so that the receptor types in the path area clear?
You mention dopamine neurons in goal directed valuation. Do all 3 competing valuation systems use dopamine? Does the aggregating circuit use this as well? It would be very interesting if you could change the balance between the 3 neurochemically.
There are three competing valuation systems, and they respond to different stimuli
Are you saying that these 3 systems are not performing valuation between the same items? Or is there a common set of items being injected into each system and the systems are performing different algorithms on the same item set? Something else? BTW, thanks for these neuroscience summaries!
The new edited volume Neuroscience of Preference and Choice includes chapters from a good chunk of the leading researchers in neuroeconomics. If you read only two books on neuroeconomics, they should be Glimcher (2010) and Neuroscience of Preference and Choice.
First, let me review the main conclusions from my Crash Course in the Neuroscience of Human Motivation:
Much of Neuroscience of Preference and Choice repeats and reinforces these conclusions. In this review, I'll focus on the major results that are discussed in Neuroscience of Preference and Choice but not in my 'Crash Course'. The editors of Neuroscience of Preference and Choice see two major results from the work reviewed in the book:
I will highlight three additional important results:
Many of the chapters do not report surprising results from the last 10 years of neuroeconomics; instead, they explain the neural mechanisms behind things we already knew about, for example common biases in decision-making, the social and emotional factors that contribute to value appraisal, the ways that context can affect preference, the ways that action can affect preference, and more.
Caplin's chapter presents not a "result" but the interesting suggestion that choice sets can be modeled as percepts. This would be an interesting result, but we'll have to wait for the tests he proposes to be performed.
The final chapter reviews the implications of the field's results for public policy.