"Expectation"

Previously in Futures, I discussed a word that we use to form an abstract futures concept: “millennium” [1]. In its most common current usage, “millennium” is an example of a word that provides, and one might even say controls, a future orientation for us. In the present essay, I am taking a different approach to the role of the word that I will be discussing. This word is not an example of a future-orientation; rather it is more of an example of language about future-orientation. The word is “expectation”. To make this distinction clearer, it may help to borrow some of the terminological distinctions made by the American logician, C.S. Peirce. First of all, for Peirce, and indeed for my present purposes, signs include words. More specifically, in a paper dated 1867, May 14th, and published in the Proceedings of the American Academy of Arts and Science (Boston), VII (1868) [2] Peirce divided signs into three categories based upon their relationship to their object—Icons, Indices, and Symbols. (Peirce himself used the convention of capitalising the words.) He defined “Icon” as a sign determined by its object “by virtue of its own internal nature”. In comparison, he defined “Index” as a sign determined by its object “by virtue of being in real relation to it”, such as when smoke is a sign of fire. A Symbol, according to Peirce, is a sign determined by its object “only in the sense that it will be so interpreted”. A Symbol thus depends upon conventions or habits

Neural Expectation Maximization

Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities. A first step towards solving these tasks is the automated discovery of distributed symbol-like representations. In this paper, we explicitly formalize this problem as inference in a spatial mixture model where each component is parametrized by a neural network. Based on the Expectation Maximization framework we then derive a differentiable clustering method that simultaneously learns how to group and represent individual entities. We evaluate our method on the (sequential) perceptual grouping task and find that it is able to accurately recover the constituent objects. We demonstrate that the learned representations are useful for next-step prediction.Comment: Accepted to NIPS 201

Growth Expectation

For a long time, changes in expectations about the future have been thought to be significant sources of economic fluctuations, as argued by Pigou (1926). Although creating such an expectation-driven cycle (the Pigou cycle) in equilibrium business cycle models was considered to be a difficult challenge, as pointed out by Barro and King (1984), recently, several researchers have succeeded in producing the Pigou cycle by balancing the tension between the wealth effect and the substitution effect stemming from the higher expected future productivity. Seminal research by Christiano, Ilut, Motto and Rostagno (2007) explains the gstock market boom-bust cycles,h characterized by increases in consumption, labor inputs, investment and the stock prices relating to high expected future technology levels, by introducing investment growth adjustment costs, habit formation in consumption, sticky prices and an inflation-targeting central bank. We, however, show that such a cycle is difficult to generate based on ggrowth expectation,h which reflect expectations of higher productivity growth rates. Thus, Barro and King's (1984) prediction still applies.Expectations, Equilibrium Business Cycle, Technological Progress