Tag: bataille

consequences of scale

Here’s some key things about an economy of control:

  • An economy of control is normally very stable. It’s punctuated equilibrium. But the mean size of disruptive events increases over time, because each of these events can cause a cascade through an ever increasingly complex system.
  • An economy of control has enormous inequalities of all kinds of scale. But there’s a kind of evenness to the inequality from an information theoretic perspective, because of a conservation of entropy principle.
  • An economy of control can be characterized adequately using third order cybernetics. It’s an unsolved research problem to determine whether third order cybernetics is reducible to second order cybernetics. There should totally be a big prize for the first person who figures this out. That prize is a very lucrative hedge fund.
  • An economy of control is, of course, characterized mainly by its titular irony: there is the minimum possible control necessary to maintain the system’s efficiency. It’s a totalizing economic model of freedom maximization.
  • Economics of control is to neoliberalism and computational social science what neoliberalism was to political liberalism and neoclassical economic theory.
  • The economy of control preserves privacy perfectly at equilibrium, barring externalities.
  • The economy of control internalizes all externalities in the long run.
  • In the economy of control, demand is anthropic.
  • In the economy of control, for any belief that needs to be shouted on television, there is a person who sincerely believes it who is willing to get paid to shout it. Journalism is replaced entirely by networks of trusted scholarship.
  • The economy of control is sociologically organized according to two diverging principles: the organizational evolutionary pressure familiar from structural functionalism, and entropy. It draws on Bataille’s theory of the general economy. But it borrows from Ulanowicz the possibility of life overcoming thermodynamics. So to speak.

Just brainstorming here.

Ascendency and overhead in networked ecosystems

Ulanowicz (2000) proposes in information-theoretic terms several metrics for ecosystem health, where one models an ecosystem as a for example a trophic network. Principal among them ascendancy , which is a measure of the extent to which energy flows in the system are predictably structured weighted by the total energy of the system. He believes that systems tend towards greater ascendancy in expectation, and that this is predictive of ecological ‘succession’ (and to some extent ecological fitness). On the other hand, overhead, which is unpredictability (perhaps, inefficiency) in energy flows (“free energy”?), are important for the system’s resiliency towards external shocks.
At least in the papers I’ve read so far, Ulanowicz is not mathematically specific about the mechanism that leads to greater ascendancy, though he sketches some explanations. Autocatalytic cycles within the network reinforce their own positive perturbations and mutations, drawing in resources from external sources, crowding out and competing with them. These cycles become agents in themselves, exerting what Ulanwicz suggests is Aristotelian final or formal causal power on the lower level components. In this way, freely floating energy is drawn into structures of increasing magnificence and complexity.

I’m reminded on Bataille’s The Accursed Share, in which he attempts to account for societal differences and the arc of human history through the use of its excess energy. “The sexual act is in time what the tiger is in space,” he says, insightfully. The tiger, as an apex predator, is flame that clings brilliantly to the less glamorous ecosystem that supports it. That is why we adore them. And yet, their existence is fragile, as it depends on both the efficiency and stability of the rest of its network. When its environment is disturbed, it is the first to suffer.
space tiger
Ulanowicz cites himself suggesting that a similar framework could be used to analyze computer networks. I have not read his account yet, though I anticipate several difficulties. He suggests that data flows in a computer network are analogous to energy flows within an ecosystem. That has intuitive appeal, but obscures the fact that some data is more valuable than others. A better analogy might be money as a substitute for energy. Or maybe there is a way to reduce both to a common currency, at least for modeling purposes.

Econophysics has been gaining steam, albeit controversially. Without knowing anything about it but based just on statistical hunches, I suspect that this comes down to using more complex models on the super duper complex phenomenon of the economy, and demonstrating their success there. In other words, I’m just guessing that the success of econophysics modeling is due to the greater degrees of freedom in the physics models compared to non-dynamic, structural equilibrium models. However, since ecology models the evolutionary dynamics of multiple competing agents (and systems of those agents), its possible that those models could capture quite a bit of what’s really going on and even be a source of strategic insight.

Indeed, economics already has a sense of stable versus unstable equilibria that resonate with the idea of stability of ecological succession. These ideas translate into game theoretic analysis as well. As we do more work with Strategic Bayesian Networks or other constructs to model equilibrium strategies in a networked, multi-agent system, I wonder if we can reproduce Ulanowicz’s results and use his ideas about ascendancy (which, I’ve got to say, are extraordinary and profound) to provide insight into the information economy.

I think that will require translating the ecosystem modeling into Judea Pearl’s framework for causal reasoning. Having been indoctrinated in Pearl’s framework in much of my training, I believe that it is general enough to subsume Ulanowicz’s results. But I have some doubt. In some of his later writings Ulanowicz refers explicitly to a “Hegelian dialectic” between order and disorder as a consequence of some of his theories, and between that and his insistence on his departure from mechanistic thinking over the course of his long career, I am worried that he may have transcended what it’s possible to do even with the modeling power of Bayesian networks. The question is: what then? It may be that once one’s work sublimates beyond our ability to model explicitly and intervene strategically, it becomes irrelevant. (I get the sense that in academia, Ulanwicz’s scientific philosophizing is a privilege reserved for someone tenured who late in their career is free to make his peace with the world in their own way) But reading his papers is so exhilarating to me. I’ve had no prior exposure to ecology before this, so his papers are packed with fresh ideas. So while I don’t know how to justify it to any of my mentors or colleagues, I think I just have to keep diving into it when I can, on the side.