I've been criticizing the standard model of economics since I was an undergraduate in 1997. The tenets of rational choice theory are not even wrong; they are simply ridiculous as a foundation for either descriptive or normative general decision making. They may be appropriate models for some specific limited set of domains and problems (like auctioning bandwidth or corporate takeovers) but even for the problems for which decision theory was originally invented (gambling) the assumptions are heinous. People just don't decide like that and they shouldn't. But even most people who use these obsolete axioms for modeling choices admit that they are woefully inadequate. The attempts of prospect theory and behavioral economics and the like to patch the holes are also laughable and usually less useful than what they fix. And while that last claim may strike some people as surprising (perhaps disappointing), what isn't news is that the homo economicus model is an unrealistic and generally unhelpful one. In fact, its failures are so widespread and obvious these days that it's cliché to even mention them.

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Statements of fact in everyday life and in science are almost certainly in one of two categories: false or vague (lacking truth value). If a statement is supposed to represent a state of the world and is true if and only if that state actually obtains in the actual existing world then of course everything is going to be false or true by coincidence, we don't have access to the actual existing world. Such a requirement for truth, however, is stupid and completely useless. An alternative is that statements purport descriptions of models we have of the world. Models have an ontology: the things that exist in that model. Models have other features to tie those elements together such as forces, laws, rules, glue, and imaginings (depending on the model). Sophisticated models, like Newtonian physics, evolutionary biology, and our implicitly held folk models of social and physical behaviors, create a vast interconnected web of relations and dependencies; a well formulated fictional world. The most we can ever expect to mean by 'true' is true in a fictional world.

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I entered an academic tournament last year called the Tournament of Party Strategies with high hopes. You may recall my dismay when a comedy of errors led to a crushing defeat (click here to read 'How my tourney experience turned into a fiasco'). Here we are a year later, and the tournament organizer has not only apologized for the snafus, but has taken several of my key suggestions into account for the next tournament. Kudos to James Fowler for making the situation right. Read his words here.

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Based on previous work proving that there is no such thing as causation and, in fact, that nothing in any scientific model corresponds or refers to anything in the "real world" we are left to consider scientific models as fictions; largely coherent and consistent collections of purported entities and relationships. Theorems of a scientific model are then true in that fictional world only and are frequently incommensurable with other theories of the same domain. Kendall Walton's idea of prompts as tools to focus collective imaginative activities applies quite accurately to equations, graphs, diagrams, demonstrations, and various other representations of parts or implications of the theory. The theory as a whole cannot all be imagined occurrently (kept in RAM in the computer analogy of the mind), but exposure to parts "sets the stage" for the consideration of further parts with the underspecified portions likely filled in with components from our folk models or other nearby scientific models.

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Network models have become very popular over the past decade. Partly this is due to a realization that network effects are important to understand the operation of many systems of interest. Partly this is due to improvements in network methodology and in the availability of computer power to support them. A feature of network analysis that continues to impede progress is its static nature: there are precious few dynamic network methods or measures of network dynamics. To be sure, this is a temporary situation as many researchers already realize this problem, including myself, and are developing techniques to capture processes of networks and on networks. One step towards that goal is to categorize differences in the myriad sorts of network models and how these differences affect processes: here I will draw attention to the degree to which network models are constrained by a physical network structure.

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Behaviorism is dead in decision theory; the whole framework only makes conceptual sense if it is described in terms of beliefs and desires directly (as convincingly argued for by James Joyce). However, decision theory itself is dead as an appropriate model for all but a few applications. Getting such a theory to do any work for predicting human behavior largely fails despite the best efforts of researchers in multiple disciplines to add ad hoc constraints and behavior-fitting mechanisms to the theory's formulation. Instead of attempting to accommodate the various forces on human action as levers in the mental decision apparatus we are better off modeling behavior as resulting from the interaction of changing sets of adaptive rules with an uncertain and dynamic environment. The idea is simply to take the benefits that more general agent-based modeling has over traditional game theory and extend those analogously to traditional decision theory. The traditional version of decision theory may excel as a normative theory of decision making under known risk in stylized situations, but for more interesting and realistic problems we need a heuristic approach.

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Moral properties seem to share many characteristics with the general class of emergent properties (described below), so it is natural to ask whether moral properties might be emergent properties of some system. I will here outline one account of how this could be and describe some corollaries that the account implies for moral theorizing. For those uninitiated in reading moral philosophy, I will do my best to provide glossary entries for the deluge of jargon; but I defer to the Stanford Online Encyclopedia and Oxford's Dictionary of Philosophy for authoritative definitions. For those already familiar with some moral philosophy I offer the following caveat: this is quite different from what's out there, but hang in there.

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Game Theory's focus on equilibria renders it inappropriate for systems where "long-term" behavior is never observed and/or intermediate states are of greater interest. Furthermore, current solution concepts force the outcomes into a single point, a set of mutually exclusive points, or a distribution over the set of mutually exclusive points. The "real world" rarely fits into these categories. The systems we model are often ongoing, dynamic, and continuous changing systems with niches, heterogeneous outcomes, and extreme sensitivity to intermediate stages. There are techniques to start exploring these realms outside game theory, and it is rather clear that for some systems game theory is simply the wrong tool for the job. Be that as it may, its long history and large following have provided game theory with a great many methods, conventions, and results that might be leveraged into solving problems and developing models of systems with the above-mentioned non-classical properties.

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Anybody who is at least a quarter awake will realize that we need to be concerned with sustainable economies and ecologies in light of burgeoning populations and limited resources. Anybody at least half awake will realize that current practices and trends are not sustainable. While the notion to be "green" is given a lot of lip service, and companies claim to be environmental friendly as a publicity technique, the sad truth is that a vast majority of companies and people really don't give a damn. If people can't even be bothered to stretch their arm a few extra inches to put paper in the recycling bin instead of the garbage can, then the future of society looks rather dim. Perhaps a complex systems approach to institutional design can find a way shift people's behavior away from destroying the planet and mankind's future.

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The genus of a topology describes the number of “handles” or holes necessary to differentiate the space. Spheres, cubes, and points all have genus zero; doughnuts (tori), coffee mugs, flatworms, and pipes have genus one; cinderblocks and eyeglass frames have genus two; etc. Up to now people have only used nonnegative integers to describe the genus, but just as fractional dimension is mathematically consistent, descriptive, and useful in many fields (including early explorations of complexity) considering fractional genus may open new avenues for exploring novel topics in mathematics, physics, and perhaps complexity.

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