Extracting Pairwise Comparisons Data from Best-Worst Scaling Surveys by Logical Inference

Best-Worst Scaling (BWS) based user interfaces allow efficient evaluation of many items through a low number of user actions. In addition to the explicit selection of the best and the worst item, more paired comparisons can be extracted from a BWS set using the unselected items. In this report, we explore how to extract further paired comparisons by logical inference between two BWS sets. Logically inferred pairs can be interpreted as implicit choices and used to validate the consistency of explicitly extracted pairs or to enrich datasets or resp. reduce data collection costs.

Measuring advertising’s effect on mental availability

This study shows that the impact of advertising on consumer memory can be observed using mental availability (MA) metrics. Four MA metrics are used to measure the effect of advertising on a brand’s mental availability, with the results showing that in the majority of cases, MA metrics are greater among both brand users and non-users who are aware of the brand’s advertising, with a greater effect among non-users. From a practical market research perspective, adding MA metrics to existing brand health tracking will have no data collection costs where brand perceptions are already being measured.

Eligibility, experience rating, and unemployment insurance take‐up

In this paper, we investigate the causes and consequences of “unclaimed” unemployment insurance (UI) benefits. A search model is developed where the costs to collecting UI benefits include both a traditional “fixed” administrative cost and an endogenous cost arising from worker and firm interactions. Experience rated taxes give firms an incentive to challenge a worker's UI claim, and these challenges are costly for the worker. Exploiting data on improper denials of UI benefits across states in the U.S. system, a two‐way fixed effects analysis shows a statistically significant negative relationship between the improper denials and the UI take‐up rate, providing empirical support for our model. We calibrate the model to elasticities implied by the two‐way fixed effects regression to quantify the relative size of these UI collection costs. The results imply that on average the costs associated with firm challenges of UI claims account for 41% of the total costs of collecting, with improper denials accounting for 8% of the total cost. The endogenous collection costs imply the unemployment rate responds much slower to changes in UI benefits relative to a model with fixed collection costs. Finally, removing all eligibility requirements and allowing workers to collect UI benefits without cost shows these costs to be 4.5% of expected output net of vacancy costs. Moreover, this change has minimal impact on the unemployment rate.

Crawling the Community Structure of Multiplex Networks

We examine the problem of crawling the community structure of a multiplex network containing multiple layers of edge relationships. While there has been a great deal of work examining community structure in general, and some work on the problem of sampling a network to preserve its community structure, to the best of our knowledge, this is the first work to consider this problem on multiplex networks. We consider the specific case in which the layers of a multiplex network have different query (collection) costs and reliabilities; and a data collector is interested in identifying the community structure of the most expensive layer. We propose MultiComSample (MCS), a novel algorithm for crawling a multiplex network. MCS uses multiple levels of multi-armed bandits to determine the best layers, communities and node roles for selecting nodes to query. We test MCS against six baseline algorithms on real-world multiplex networks, and achieved large gains in performance. For example, after consuming a budget equivalent to sampling 20% of the nodes in the expensive layer, we observe that MCS outperforms the best baseline by up to 49%.

Menggali Potensi Sumber Pendapatan Asli Daerah Kabupaten Pegunungan Arfak Provinsi Papua Barat Melalui Analytical Hierarchy Process (AHP)

The Arfak Mountains Regency is administratively formed in 2012. With the establishment of the Arfak Mountains District as a new autonomous region, all forms of economic levels have been separated from the former regency. Arfak Mountains Regency as an autonomous region is expected to have the ability to manage regional own revenue to finance the administration of the regional government. In achieving the capability in regional financial independence, it is necessary to optimize the sources of regional revenue. The results of the Analytical Hierarchy Process (AHP) on the assessment of several alternative policies related to the optimization of sources of regional own revenue in the Arfak Mountains District, are as followed: (1). Increasing revenue capacity through better planning; (2). expanding revenue bases; (3). improving administrative efficiency and reducing collection costs; (4). strengthening collection processes and (5). improving controlling processes. The success of the management of regional own revenue is not only measured by the amount of revenue that can be achieved, but also by the extent to which the regional own revenue can play a role in driving the economy of the community so that it can improve the welfare of the community in this regency.