Wiki-worthy: collective judgment of candidate notability

The use of socio-technical data to predict elections is a growing research area. We argue that election prediction research suffers from under-specified theoretical models that do not properly distinguish between ‘poll-like’ and ‘prediction market-like’ mechanisms understand findings. More specifically, we argue that, in systems with strong norms and reputational feedback mechanisms, individuals have market-like incentives to bias content creation toward candidates they expect will win. We provide evidence for the merits of this approach using the creation of Wikipedia pages for candidates in the 2010 US and UK national legislative elections. We find that Wikipedia editors are more likely to create Wikipedia pages for challengers who have a better chance of defeating their incumbent opponent and that the timing of these page creations coincides with periods when collective expectations for the candidate's success are relatively high.     

Lurking Behavior in Online Psychosocial Discussion Forums: Theoretical Perspectives and Implications for Practice

Improving outcomes of telebehavioral psychoeducation requires rethinking program design when delivered wholly or partially for self-directed participation. Discussion forum participation often follows the “90-9-1 Rule” where 90% of participants lurk, rather than contribute content. A theoretical perspective on the behavior can help explain its adaptive functions, as well as the threats that this behavior poses to the lurker. Implications for practice require program redesign that actively links individual skills training and group-based discussion. The proposed linking design can synergize individual and group participation to support the development of mutual aid, as well as greater interaction with psychoeducation content and materials.     

Scheduling in a Management Context: Uncertain Processing Times and Non-Regular Performance Measures*

Decision makers often face scheduling problems in which processing times are not known with certainty. Non-regular performance measures, in which both earliness and tardiness are penalized, are also becoming more common in both manufacturing and service operations. We model a managerial environment with task processing times (which include sequenceindependent set-up times) prescribed by three-parameter lognormal distributions. Upon completion, each task derives a reward given by a particular piecewise-linear reward function. The objective is to select a sequence of tasks maximizing the expected total reward. The relative generality of the problem renders many enumerative methods inapplicable or computationally intractable. To overcome such difficulties we develop efficient priorityinduced construction (PIC) heuristics which build up a complete schedule by inserting tasks (singly from a list) into a partial sequence of tasks. In each partial and complete sequence a period of idle time is permitted prior to the first task. Performance on realistic-sized problems is very encouraging, with cost penalties averaging less than one percent.     

Stochastic and Intransitive Behavior in a State-Preference Model of Asset Choice*

Most models of investor behavior assume a time-state independent utility function and result in a deterministic solution where a given set of inputs uniquely specifies the decision. In contrast, a state preference model using a time-state dependent utility function is derived in this paper. The model allows the investment choice decision to be analyzed in a game theoretic context. The general solution is a mixed strategy which allows for a probabilistic interpretation of the decision. The approach presented in this paper can accommodate anomalies such as intransitivity of preference and satisficing as rational behavior. An example of a possible implementation is given along with interpretations of the outcomes.     

A Decision Model for the Robot Selection Problem Using Robust Regression*

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.     

From low to high fertility in Sulawesi (Indonesia) during the colonial period: explaining the 'first fertility transition'

This paper examines the past transition from low to high fertility which, in Indonesia as elsewhere, preceded the return to lower birth rates. Data from two parts of the island of Sulawesi where fertility rose during the colonial period are used to explain both why it rose, and why it was originally low. Economic conditions, it is argued, were the most important factors, affecting fertility via the supply of income and the demand for labour. Two schematic models of the 'first fertility transition' are proposed. In areas with low population densities and area-extensive forms of agriculture responsive to commercial stimuli, birth rates rose as the growth of commerce raised levels of prosperity, facilitated marriage, and undermined institutions such as debt-slavery which had previously acted to restrict marital fertility. In densely populated areas with labour-intensive agriculture and heavy state taxation in labour, fertility rose in response to demands for women's (and possibly child) labour that did not necessarily lead to gains in income.     

An Examination of the Effect of Continuous Quality Improvements on Optimal Pricing for Durable Goods*

This paper investigates the nature of optimal prices for a durable good in the presence of continuous quality improvements. The analysis of optimal prices is based on a nonlinear dynamic model of sales response that relates price, quality, average life of a product and the persistence of quality perceptions. Numerical solutions to the model are derived by employing the generalized reduced gradient algorithm. The results show that optimal price depends on the persistence of quality perceptions and the average life of a product (an aspect of quality). The analysis of optimal results affirms results based on other models and provides insights on the influence that quality has on optimal pricing. The implications of the results and suggestions for future research are discussed.