Sequential decision analysis for nonstationary stochastic processes

A formulation of the problem of making decisions concerning the state of nonstationary stochastic processes is given. An optimal decision rule, for the case in which the stochastic process is independent of the decisions made, is derived. It is shown that this rule is a generalization of the Bayesian likelihood ratio test; and an analog to Wald's sequential likelihood ratio test is given, in which the optimal thresholds may vary with time

Innovation decision of Tunisian service firms: an empirical analysis

Innovation is widely recognised as a key driver of economic growth and competitiveness. But, some works focus especially on analyzing the determinants and the effects of innovation while distinguishing between its various types (product innovation, process innovation, radical innovation and incremental innovation). The analysis of the determinants is certainly important, but few research efforts testing the way in which firms make the decision to innovate. Based on a sample of 108 Tunisian service firms, the purpose of the paper is to explain the way in which firms make the decision to innovate: simultaneous (one-stage model) or sequential (two-stage model). We find that the two-stage model has a statistically-significant advantage in predicting the innovation. In practice, the sequential model illustrates well the innovation making-decision procedures.Innovation, Decision making, Service sector.

SUPPLY RESPONSE AND IMPACT OF GOVERNMENT-SUPPORTED CROPS ON THE TEXAS VEGETABLE INDUSTRY

Supply functions, elasticity estimates, and nonjointness test results consistently indicated that few commodities compete economically in the production of six major Texas vegetables (cabbage, cantaloupes, carrots, onions, potatoes, and watermelons). Significant bias effects caused by government-supported commodities, fixed inputs, and technological change were observed and measured. Nonnested test results for the hypothesis of sequential decision making by vegetable producers were inconclusive, but they gave greater likelihood support to sequential than to contemporaneous decision making.Demand and Price Analysis,

Decision map for spatial decision making in urban planning

In this paper, we introduce the concept of decision map and illustrate the way this new concept can be used effectively to support participation in spatial decision making and in urban planning. First, we start by introducing our spatial decision process which is composed of five, non-necessary sequential, phases: problem identification and formulation, analysis, negotiation, concertation, and evaluation and choice. Negotiation and concertation are two main phases in spatial decision making but most available frameworks do not provide tools to support them effectively. The solution proposed here is based on the concept of decision map which is defined as an advanced version of conventional geographic maps which is enriched with preferential information and especially designed to clarify decision making. It looks like a set of homogenous spatial units; each one is characterised with a global, often ordinal, evaluation that represents an aggregation of several partial evaluations relative to different criteria. The decision map is also enriched with different spatial data exploration tools. The procedure of the construction of a decision map contains four main steps: definition of the problem (i.e. generation of criteria maps), generation of an intermediate map, inference of preferential parameters, and generation of a final decision map. The concept of decision map as defined here is a generic tool that may be applied in different domains. This paper focuses on the role of the decision map in supporting participation in spatial decision making and urban planning. Indeed, the decision map is an efficient communication tool in the sense that it permits to the different groups implied in the spatial decision process to ‘think visually’ and to communicate better between each other.ou

Abraham Wald

This paper grew out of a lecture presented at the 54th Session of the International Statistical Institute in Berlin, August 13 - 20, 2003, Schneeweiss (2003). It intends not only to outline the eventful life of Abraham Wald (1902 - 1950) in Austria and in the United States but also to present his extensive scientific work. In particular, the two main subjects, where he earned most of his fame, are outline: Statistical Decision Theory and Sequential Analysis. In addition, emphasis is laid on his contributions to Econometrics and related fields

Stochastic models of evidence accumulation in changing environments

Organisms and ecological groups accumulate evidence to make decisions. Classic experiments and theoretical studies have explored this process when the correct choice is fixed during each trial. However, we live in a constantly changing world. What effect does such impermanence have on classical results about decision making? To address this question we use sequential analysis to derive a tractable model of evidence accumulation when the correct option changes in time. Our analysis shows that ideal observers discount prior evidence at a rate determined by the volatility of the environment, and the dynamics of evidence accumulation is governed by the information gained over an average environmental epoch. A plausible neural implementation of an optimal observer in a changing environment shows that, in contrast to previous models, neural populations representing alternate choices are coupled through excitation. Our work builds a bridge between statistical decision making in volatile environments and stochastic nonlinear dynamics.Comment: 26 pages, 7 figure