Showing papers by "William G. Macready published in 2000"

Optimal search on a technology landscape

Abstract: We address the question of how a firm’s current location in the space of technological possibilities constrain its search for technological improvements. We formalize a quantitative notion of distance between technologies — encompassing the distinction between evolutionary changes (small distance) versus revolutionary change (large distance) — and introduce a technology landscape into an otherwise standard dynamic programming setting where the optimal strategy is to assign a reservation price to each possible technology. Technological search is modeled as movement, constrained by the cost of search, on a technology landscape. Simulations are presented on a stylized technology landscape while analytic results are derived using landscapes that are similar to Markov random fields. We find that early in the search for technological improvements, if the initial position is poor or average, it is optimal to search far away on the technology landscape; but as the firm succeeds in finding technological improvements it is optimal to confine search to a local region of the landscape. © 2000 Elsevier Science B.V. All rights reserved.

Self-dissimilarity: an empirically observable complexity measure

Abstract: For systems usually characterized as \complex/living/intelligent" very often the spatio-temporal patterns exhibited on di erent scales di er markedly from one another. For example the biomass distribution of a human body \looks very di erent" depending on the spatial scale at which one examines that biomass. Conversely, the density patterns at di erent scales in \dead/simple" systems (e.g., gases, mountains, crystals) do not vary signi cantly from one another. Accordingly, the degrees of self-dissimilarity between various scales constitutes a complexity \signature" of the system. Such signatures can be empirically measured for many real-world data sets concerning spatio-temporal densities, be they mass densities, species densities, or symbol densities. This allows us to compare the complexity signatures of wholly di erent kinds of systems (e.g., systems involving information density in a digital computer, vs. species densities in a rainforest, vs. capital density in an economy, etc.). Such signatures can also be clustered, to provide an empirically determined taxonomy of \kinds of systems" that share organizational traits. The precise measure of dissimilarity between scales that we propose is the amount of extra information on one scale beyond that which exists on a di erent scale. This \added information" is perhaps most naturally determined using a maximum entropy inference of the distribution of patterns at the second scale, based on the provided distribution at the rst scale. We brie y discuss using our measure with other inference mechanisms (e.g., Kolmogorov complexity-based inference).