Measuring the Uncertain – Remarks on Entropy and Information

According to original Shannon"s concept entropy is the measure assigned to the spectrum of possible states of a given system. A cognitive subject can reduce his/her uncertainty through gaining some information. Incoming information can change the subject"s knowledge in two ways: it may either eliminate some possible states or it can change the way the subject "partitions" the world. The former situation corresponds to gaining some knowledge in the way of empirical examining the world – the result of an experiment reduces our uncertainty of the world by selecting of the whole spectrum of expectable answers to the "experimental question� those which may be the most probable. We claim that the expectable answers are not declarations stated by "the World� but result from our ability predict "the World"s� behavior. Such predictions are based upon a certain metaphysical model of "the World� as well as upon the theoretical background

Evidential-EM Algorithm Applied to Progressively Censored Observations

Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incom-plete data, where the loss of information is due to both mixture models and censored observations. The prior uncertain information is expressed by belief functions, while the pseudo-likelihood function is derived based on imprecise observations and prior knowledge. Then E2M method is evoked to maximize the generalized likelihood function to obtain the optimal estimation of parameters. Numerical examples show that the proposed method could effectively integrate the uncertain prior infor-mation with the current imprecise knowledge conveyed by the observed data

Weddings with Uncertain Prospects – Mergers under Asymmetric Information

We provide a framework for analyzing bilateral mergers when there is two-sided asymmetric information about firms’ types. We show that there is always a "no-merger" equilibrium where firms do not consent to a merger, irrespective of their type. There may also be a "cut-off" equilibrium if the expected merger returns satisfy a suitable single crossing condition, which will hold if a firm’s merger returns are "essentially monotone decreasing" in its type. Applying our analysis to the linear Cournot model, we show how the merger pattern depends on the cost effects of mergers, the extent of uncertainty, and the way profits are split. Specifically, we show how increasing uncertainty about competitor types may foster mergers as firms hope for strong rationalization effects.merger, asymmetric information, oligopoly, single crossing

Regulatory Mechanisms and Information Processing in Uncertain Fisheries

We study the effects on fisherman decision processes of periodic (e.g., weekly) individual quotas. In the model, the fisherman must choose at the start of each week which of two grounds to fish on. The catch per week on each ground is a random variable and the fisherman does not know with certainty the parameters of the distribution of that variable. He does have estimates on each parameter and can improve these estimates by Bayesian updating. The choice of a fishing ground takes into account the expected catch on that ground and the expected improvement in information from fishing on that ground. Our study is concerned with the effect of weekly quotas on the joint production of information and fish. Various policy implications are discussed, and the results are compared with the policy analysis of Clark (1980) in the deterministic case. We show that the quota affects the value of Information and that if quotas are transferable, then the quota may limit its own value.Environmental Economics and Policy, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy, Risk and Uncertainty,