Bargaining is a ubiquitous feature of social and economic interactions.
The book pursues two main goals. From a methodological point of view, game theoretic models and agent-based models are comparatively analysed. This provides an insightful case study about the vices and virtues of both methods with regards to the trade-off between analytic rigour and descriptive detail. From a practical point of view, valuable insights can be drawn about different strategies’ success, fairness and efficiency. It is shown how those properties change with environmental circumstances.
DARIAH (Digital Research Infrastructure for the Arts and Humanities) is part of the European Strategy on Research Infrastructures. Among 38 projects originally on this roadmap, DARIAH is one of two projects addressing social sciences and humanities. According to its self-conception and its political mandate DARIAH has the mission to enhance and support digitally-enabled research across the humanities and arts. DARIAH aims to develop and maintain an infrastructure in support of ICT-based research practices. One main distinguishing aspect of DARIAH is that it is not focusing on one application domain but especially addresses the support of interdisciplinary research in the humanities and arts. The present paper first gives an overview on DARIAH as a whole and then focuses on the important aspect of technical, syntactic and semantic interoperability. Important aspects in this respect are metadata registries and crosswalk definitions allowing for meaningful cross-collection and inter-collection services and analysis.
There is evidence that survey interviewers may be tempted to manipulate answers to filter questions in a way that minimizes the number of follow-up questions. This becomes relevant when ego-centered network data are collected. The reported network size has a huge impact on interview duration if multiple questions on each alter are triggered. We analyze interviewer effects on a network-size question in the mixed-mode survey 'Panel Study 'Labour Market and Social Security'' (PASS), where interviewers could skip up to 15 follow-up questions by generating small networks. Applying multilevel models, we find almost no interviewer effects in CATI mode, where interviewers are paid by the hour and frequently supervised. In CAPI, however, where interviewers are paid by case and no close supervision is possible, we find strong interviewer effects on network size. As the area-specific network size is known from telephone mode, where allocation to interviewers is random, interviewer and area effects can be separated. Furthermore, a difference-in-difference analysis reveals the negative effect of introducing the follow-up questions in Wave 3 on CAPI network size. Attempting to explain interviewer effects we neither find significant main effects of experience within a wave, nor significantly different slopes between interviewers.
Panel surveys suffer from attrition. Most panel studies use propensity models or weighting class approaches to correct for non-random dropout. These models draw on variables measured in a previous wave or from paradata of the study. While it is plausible that they affect contactability and cooperativeness, panel studies usually cannot assess the impact of events between waves on attrition. The amount of change in the population could be seriously underestimated if such events had an effect on participation in subsequent waves. The panel study PASS is a novel dataset for labour market and poverty research. In PASS, survey data on (un)employment histories, income and education of participants are linked to corresponding data from respondents' administrative records. Thus, change can be observed for attritors as well as for continued participants. These data are used to show that change in household composition, employment status or receipt of benefits has an influence on contact and cooperation rates in the following wave. A large part of the effect is due to lower contactability of households who moved. Nevertheless, this effect can lead to biased estimates for the amount of change. After applying the survey's longitudinal weights this bias is reduced, but not entirely eliminated.