The notion that there is a path from reciprocal altruism to big brains and advanced cognitive capabilities leads us to ask whether we can create “effective” procedures that shed additional light on the suppositions that are involved, and their consequences. Any skepticism about some virulent kind of scientism then gets whisked away by the imposition of a procedure combined with an earnest interest in careful evaluation of the outcomes. That may not be enough, but it is at least a start.
I turn back to Marcus Hutter, Solomonoff, and Chaitin-Kolmogorov at this point. I’ll be primarily referencing Hutter’s Universal Algorithmic Intelligence (A Top-Down Approach) in what follows. And what follows is an attempt to break down how three separate factors related to intelligence can be explained through mathematical modeling. The first and the second are covered in Hutter’s paper, but the third may represent a new contribution, though perhaps an obvious one without the detail work that is needed to provide good support.
First, then, we start with a core requirement of any goal-seeking mechanism: the ability to predict patterns in the environment external to the mechanism. This is well-covered since Solomonoff in the 60s who formalized the implicit arguments in Kolmogorov algorithmic information theory (AIT), and that were subsequently expanded on by Greg Chaitin. In essence, given a range of possible models represented by bit sequences of computational states, the shortest sequence that predicts the observed data is also the optimal predictor for any future data also produced by the underlying generator function. The shortest sequence is not computable, but we can keep searching for shorter programs and come up with unique optimizations for specific data landscapes. And that should sound familiar because it recapitulates Occam’s Razor and, in a subset of cases, Epicurus’ Principle of Multiple Explanations. This represents the floor-plan of inductive inference, but it is only the first leg of the stool.
We should expect that evolutionary optimization might work according to this abstract formulation, but reality always intrudes. Instead, evolution is saddled by historical contingency that channels the movements through the search space. Biology ain’t computer science, in short, if for no other reason than it is tied to the physics and chemistry of the underlying operating system. Still the desire is there to identify such provable optimality in living systems because evolution is an optimizing engine, if not exactly an optimal one.
So we come to the second factor: optimality is not induction alone. Optimality is the interaction between the predictive mechanism and the environment. The “mechanism” might very well provide optimal or near optimal predictions of the past through a Solomonoff-style model, but applying those predictions introduces perturbations to the environment itself. Hutter elegantly simplifies this added complexity by abstracting the environment as a computing machine (a logical device; we assume here that the universe behaves deterministically even where it may have chaotic aspects) and running the model program at a slightly slower rate than the environmental program (it lags). Optimality is then a utility measure that combines prediction with resource allocation according to some objective function.
But what about the third factor that I promised and is missing? We get back to Fukuyama and the sociobiologists with this one: social interaction is the third factor. The exchange of information and the manipulation of the environment by groups of agents diffuses decision theory over inductive models of environments into a group of “mechanisms” that can, for example, transmit the location of optimal resource availability among the clan as a whole, increasing the utility of the individual agents with little cost to others. It seems appealing to expand Hutter’s model to include a social model, an agent model, and an environment within the purview of the mathematics. We might also get to the level where the social model overrides the agent model for a greater average utility, or where non-environmental signals from the social model interrupt function of the agent model, representing an irrational abstraction with group-selective payoff.