Suchergebnisse

"Reputation in Deterrence and Decision-Making" published on by Oxford University Press.

The functions and powers of local self-governance are broadly discussed at all levels of government institutions,non-governmental organizations and communities. There is an expressed position that local communities andtheir local self-governing institutions should be given the power of subsidiary decision making in locally specificissues. However, year after year, the unanimous attitude is suppressed by financial and fiscal dependence upon thecentral government, unreasonably large territorial units with high population density. These circumstances limitdown the decision-making and, moreover, the implementation of the decisions. From this point of view, the financialand fiscal powers of local self-governing institutions and, consequently, the decision-making strata are significantlydifferent from those in other Baltic and Central European countries, as well as Scandinavian states. This articledeals with the analysis of local self-governing administration in terms of its fiscal and financial empowerment, thesize of the local communities and its impact on decision-making. A comparison is drawn with Scandinavian, centralEuropean and Baltic countries.

AbstractEmpirical evidence suggests that decision‐makers are ill‐equipped to identify all relevant objectives in a decision problem. We examine the effect of an incomplete set of objectives using a Monte Carlo simulation to compare a baseline model to a reduced model incorporating only a subset of objectives. We assess the performance of reduced models varying in the number of objectives, the number of alternatives, the correlations among objectives, and attribute weights. Results suggest that missing objectives will most impact multiattribute models with negative correlations between objectives; similarly, models with equally weighted objectives suffer more than models with unequal weights. Decision problems with more objectives tend to be less impacted by missing objectives, given the same proportion of missing objectives. In contrast, decision problems with more alternatives are more impacted for some performance measures but less on others. However, the variation in model performance due to the number of objectives and alternatives is relatively minor compared to the variation due to the nature of the correlation between objectives.

Within the realm of e-government, the development has moved towards testing new means for democratic decisionmaking, like e-panels, electronic discussion forums, and polls. Although such new developments seem promising, they are not problem-free, and the outcomes are seldom used in the subsequent formal political procedures. Nevertheless, process models offer promising potential when it comes to structuring and supporting transparency of decision processes in order to facilitate the integration of the public into decision-making procedures in a reasonable and manageable way. Based on real-life cases of urban planning processes in Sweden, we present an outline for an integrated framework for public decision making to: a) provide tools for citizens to organize discussion and create opinions; b) enable governments, authorities, and institutions to better analyse these opinions; and c) enable governments to account for this information in planning and societal decision making by employing a process model for structured public decision making.

ABSTRACTA learning‐by‐example algorithm, the ordinal learning model (OLM), that automatically generates symbolic rule‐bases from examples was applied to four real‐world multiattribute ordinal problem domains. The model automatically generates consistent and irredundant symbolic classification rules that mimic, in many aspects, the behavior of human subjects who solved similar problems during empirical studies. The OLM's performance is compared with those of regression analysis and with C4, a well‐known symbolic learning‐by‐example decision tree building algorithm. The OLM uses mainly comparison operations and does not attempt to optimize the rule‐bases it generates. Yet, the results show that the OLM's predictions are very accurate and the resulting rule‐bases are relatively compact. The time required for constructing the rule‐bases via the OLM was very competitive as well.

ABSTRACTConventionally, elements of a multiattribute utility model characterizing a decision maker's preferences, such as attribute weights and attribute utilities, are treated as deterministic, which may be unrealistic because assessment of such elements can be imprecise and erroneous, or differ among a group of individuals. Moreover, attempting to make precise assessments can be time consuming and cognitively demanding. We propose to treat such elements as stochastic variables to account for inconsistency and imprecision in such assessments. Under these assumptions, we develop procedures for computing the probability distribution of aggregate utility for an additive multiattribute utility function (MAUF), based on the Edgeworth expansion. When the distributions of aggregate utility for all alternatives in a decision problem are known, stochastic dominance can then be invoked to filter inferior alternatives. We show that, under certain mild conditions, the aggregate utility distribution approaches normality as the number of attributes increases. Thus, only a few terms from the Edgeworth expansion with a standard normal density as the base function will be sufficient for approximating an aggregate utility distribution in practice. Moreover, the more symmetric the attribute utility distributions, the fewer the attributes to achieve normality. The Edgeworth expansion thus can provide a basis for a computationally viable approach for representing an aggregate utility distribution with imprecisely specified attribute weights and utilities assessments (or differing weights and utilities across individuals). Practical guidelines for using the Edgeworth approximation are given. The proposed methodology is illustrated using a vendor selection problem.