After the Cybernetic Totalism talk, the Emergent Epistemology Salon wanted to hunt around for something brilliantly new in the ballpark of "general reasoning" that could actually be implemented. To that end we looped back to something we talked about back in May of 2007 and met again on on Jan 13, 2008 to talk about...
Manifesto for an Evolutionary Economics of Intelligence
Eric B. Baum, 1998
PARTIAL ABSTRACT: We address the problem of reinforcement learning in ultra-complex environments. Such environments will require a modular approach. The modules must solve subproblems, and must collaborate on solution of the overall problem. However a collection of rational agents will only collaborate if appropriate structure is imposed. We give a result, analogous to the First Theorem of Welfare Economics, that shows how to impose such structure. That is, we describe how to use economic principles to assign credit and ensure that a collection of rational (but possibly computationally limited) agents will collaborate on reinforcement learning. Conversely, we survey catastrophic failure modes that can be expected in distributed learning systems, and empirically have occurred in biological evolution, real economics, and artificial intelligence programs, when such structure was not enforced.
We conjecture that simulated economies can evolve to reinforcement learn in complex environments in feasible time scales, starting from a collection of agents which have little knowledge and hence are *not* rational. We support this with two implementations of learning models based on these principles.
Compared to the previous discussion on this paper, we were more focused on the algorithm itself instead of on the broader claims about the relevance of economics to artificial intelligence. We were also more focused on a general theme the group has been following - the ways that biases in an optimizing process are (or are not) suited to the particularities of of given learning problem.
We covered some of the same ideas related to the oddness that a given code fragment within the system needed both domain knowledge and "business sense" in order to survive. Brilliant insights that are foolishly sold for less than their CPU costs might be deleted and at the same time, the potential for "market charlatanism" might introduce hiccups in the general system's ability to learn. By analogy to the real world, is it easier to invent an economically viable fusion technology or to defraud investors with a business that falsely claims to have an angle on such technology?
We also talked about the reasons real economies are so useful - they aggregate information from many contexts and agents into a single data point (price) that can be broadcast to all contexts to help agents in those contexts solve their local problems more effectively. It's not entirely clear how well the analogy from real economies maps into the idea of a general learning algorithm. You already have a bunch of agents in the real world. And there's already all kinds of structure (physical distance and varied resources and so on) in the world. The scope of the agents is already restricted to what they have at hand and the expensive problem that economics solves is "getting enough of the right information to all the dispersed agents". In a computer, with *random access* memory, the hard part is discovering and supporting structure in giant masses of data the first place. It seemed that economic inspiration might be a virtue in the physical world due the necessities imposed by the physical world. Perhaps something more cleanly mathematical would be better inside a computer?
Finally, there was discussion around efforts to re-implement the systems described in the paper and how different re-implementation choices might improve or hurt the performance.