Associative processing and paranormal belief

In the present study we introduce a novel task for the quantitative assessment of both originality and speed of individual associations. This 'BAG' (Bridge-the-Associative-Gap) task was used to investigate the relationships between creativity and paranormal belief. Twelve strong 'believers' and 12 strong 'skeptics' in paranormal phenomena were selected from a large student population (n > 350). Subjects were asked to produce single-word associations to word pairs. In 40 trials the two stimulus words were semantically indirectly related and in 40 other trials the words were semantically unrelated. Separately for these two stimulus types, response commonalities and association latencies were calculated. The main finding was that for unrelated stimuli, believers produced associations that were more original (had a lower frequency of occurrence in the group as a whole) than those of the skeptics. For the interpretation of the result we propose a model of association behavior that captures both 'positive' psychological aspects (i.e., verbal creativity) and 'negative' aspects (susceptibility to unfounded inferences), and outline its relevance for psychiatry. This model suggests that believers adopt a looser response criterion than skeptics when confronted with 'semantic noise'. Such a signal detection view of the presence/absence of judgments for loose semantic relations may help to elucidate the commonalities between creative thinking, paranormal belief and delusional ideation.

Intuitionistic common knowledge or belief

Starting off from the usual language of modal logic for multi-agent systems dealing with the agents’ knowledge/belief and common knowledge/belief we define so-called epistemic Kripke structures for intu- itionistic (common) knowledge/belief. Then we introduce corresponding deductive systems and show that they are sound and complete with respect to these semantics.

An examination of the link between just world belief and schadenfreude

Schadenfreude is a pleasure derived from someone else’s misfortune. Just world belief is a desire to belief that people get what they deserve and deserve what they get (Lerner, 1965,1980). Interestingly, previous scholars documented the link between schadenfreude, responsibility and deservingness (e.g. van Dijk, Goslinga, & Ouwerkerk, 2008), i.e. the more failure is deserved, the more perceived responsibility for the failure, and subsequently more schadenfreude is evoked. Thus, the present study tested if a threat of a just world belief intensifies experience of schadenfreude. The participants (N=48, 31 women and 17 men, M age = 23.72), were randomly assigned to one of two experimental conditions (just world belief: threat versus no-threat) between-participant design. They read scenarios which were designed to threaten or maintain their just world belief. Next, they were transferred to an online magazine presenting funny stories about other peoples’ failures. The stories were selected in a pilot study in order to evoke schadenfreude. As presumed, the participants exposed to the threat of just world belief experienced more schadenfreude, i.e. spent more time on reading schadenfreude stories. The results confirmed the existence of a link between just world threat and schadenfreude.

Schadenfreude and just world belief

The present study tested the hypothesis that a threat of a just world belief intensifies experience of schadenfreude (i.e., pleasure at another's misfortune). The participants read scenarios which were designed to threaten or maintain their just world belief. Subsequently, they were transferred to an online magazine presenting funny stories about other peoples' failures. As presumed, the participants exposed to the threat of just world belief spent more time on reading. These results confirmed the existence of a link between just world threat and schadenfreude.

Beyond belief : Strategic taboos and organizational identity in strategic agenda setting

A comprehensive strategic agenda matters for fundamental strategic change. Our study seeks to explore and theorize how organizational identity beliefs influence the judgment of strategic actors when setting an organization's strategic agenda. We offer the notion of "strategic taboo" as those strategic options initially disqualified and deemed inconsistent with the organizational identity beliefs of strategic actors. Our study is concerned with how strategic actors confront strategic taboos in the process of setting an organization's strategic agenda. Based on a revelatory inductive case study, we find that strategic actors engage in assessing the concordance of the strategic taboos with organizational identity beliefs and, more specifically, that they focus on key identity elements (philosophy; priorities; practices) when doing so. We develop a typology of three reinterpretation practices that are each concerned with a key identity element. While contextualizing assesses the potential concordance of a strategic taboo with an organization's overall philosophy and purpose, instrumentalizing assesses such concordance with respect to what actors deem an organization's priorities to be. Finally, normalizing explores concordance with respect to compatibility and fit with the organization's practices. We suggest that assessing concordance of a strategic taboo with identity elements consists in reinterpreting collective identity beliefs in ways that make them consistent with what organizational actors deem the right course of action. This article discusses the implications for theory and research on strategic agenda setting, strategic change, a practice-based perspective on strategy, and on organizational identity.

FACTORS INFLUENCING CHOICE OF CONTRACEPTIVE METHOD AMONG MARRIED FECUND WOMEN WHO INTEND NO ADDITIONAL BIRTHS: HEALTH BELIEF MODEL AND ECONOMIC PERSPECTIVES

This research examined to what extent Health Belief Model (HBM) and socioeconomic variables were useful in explaining the choice whether or not more effective contraceptive methods were used among married fecund women intending no additional births. The source of the data was the 1976 National Survey of Family Growth conducted under the auspices of the National Center for Health Statistics. Using the HBM as a framework for multivariate analyses limited support was found (using available measures) that the HBM components of motivation and perceived efficacy influence the likelihood of more effective contraceptive method use. Support was also found that modifying variables suggested by the HBM can influence the effects of HBM components on the likelihood of more effective method use. Socioeconomic variables were found, using all cases and some subgroups, to have a significant additional influence on the likelihood of use of more effective methods. Limited support was found for the concept that the greater the opportunity costs of an unwanted birth the greater the likelihood of use of more effective contraceptive methods. This research supports the use of HBM and socioeconomic variables to explain the likelihood of a protective health behavior, use of more effective contraception if no additional births are intended.^

Optimized edge appearance probability for cooperative localization based on tree-reweighted nonparametric belief propagation

Nonparametric belief propagation (NBP) is a well-known particle-based method for distributed inference in wireless networks. NBP has a large number of applications, including cooperative localization. However, in loopy networks NBP suffers from similar problems as standard BP, such as over-confident beliefs and possible nonconvergence. Tree-reweighted NBP (TRW-NBP) can mitigate these problems, but does not easily lead to a distributed implementation due to the non-local nature of the required so-called edge appearance probabilities. In this paper, we propose a variation of TRWNBP, suitable for cooperative localization in wireless networks. Our algorithm uses a fixed edge appearance probability for every edge, and can outperform standard NBP in dense wireless networks.

Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing

Belief propagation (BP) is a technique for distributed inference in wireless networks and is often used even when the underlying graphical model contains cycles. In this paper, we propose a uniformly reweighted BP scheme that reduces the impact of cycles by weighting messages by a constant ?edge appearance probability? rho ? 1. We apply this algorithm to distributed binary hypothesis testing problems (e.g., distributed detection) in wireless networks with Markov random field models. We demonstrate that in the considered setting the proposed method outperforms standard BP, while maintaining similar complexity. We then show that the optimal ? can be approximated as a simple function of the average node degree, and can hence be computed in a distributed fashion through a consensus algorithm.

Uniformly Reweighted Belief Propagation: A Factor Graph Approach

Tree-reweighted belief propagation is a message passing method that has certain advantages compared to traditional belief propagation (BP). However, it fails to outperform BP in a consistent manner, does not lend itself well to distributed implementation, and has not been applied to distributions with higher-order interactions. We propose a method called uniformly-reweighted belief propagation that mitigates these drawbacks. After having shown in previous works that this method can substantially outperform BP in distributed inference with pairwise interaction models, in this paper we extend it to higher-order interactions and apply it to LDPC decoding, leading performance gains over BP.

Reducing communication overhead for cooperative localization using nonparametric belief propagation

A number of methods for cooperative localization has been proposed, but most of them provide only location estimate, without associated uncertainty. On the other hand, nonparametric belief propagation (NBP), which provides approximated posterior distributions of the location estimates, is expensive mostly because of the transmission of the particles. In this paper, we propose a novel approach to reduce communication overhead for cooperative positioning using NBP. It is based on: i) communication of the beliefs (instead of the messages), ii) approximation of the belief with Gaussian mixture of very few components, and iii) censoring. According to our simulations results, these modifications reduce significantly communication overhead while providing the estimates almost as accurate as the transmission of the particles.

Distributed target tracking based on belief propagation consensus

Distributed target tracking in wireless sensor networks (WSN) is an important problem, in which agreement on the target state can be achieved using conventional consensus methods, which take long to converge. We propose distributed particle filtering based on belief propagation (DPF-BP) consensus, a fast method for target tracking. According to our simulations, DPF-BP provides better performance than DPF based on standard belief consensus (DPF-SBC) in terms of disagreement in the network. However, in terms of root-mean square error, it can outperform DPF-SBC only for a specific number of consensus iterations.

B2DI a bayesian BDI agent model with causal belief updating based on MSBN

In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of these beliefs is updated based on MSBN. The classical BDI agent architecture has been extended in order to manage uncertainty using Bayesian reasoning. The resulting extended model, so-called B2DI, proposes a new control loop. The proposed B2DI model has been evaluated in a network fault diagnosis scenario. The evaluation has compared this model with two previously developed agent models. The evaluation has been carried out with a real testbed diagnosis scenario using JADEX. As a result, the proposed model exhibits significant improvements in the cost and time required to carry out a reliable diagnosis.

Cooperative localization in mobile networks using nonparametric variants of belief propagation

Of the many state-of-the-art methods for cooperative localization in wireless sensor networks (WSN), only very few adapt well to mobile networks. The main problems of the well-known algorithms, based on nonparametric belief propagation (NBP), are the high communication cost and inefficient sampling techniques. Moreover, they either do not use smoothing or just apply it o ine. Therefore, in this article, we propose more flexible and effcient variants of NBP for cooperative localization in mobile networks. In particular, we provide: i) an optional 1-lag smoothing done almost in real-time, ii) a novel low-cost communication protocol based on package approximation and censoring, iii) higher robustness of the standard mixture importance sampling (MIS) technique, and iv) a higher amount of information in the importance densities by using the population Monte Carlo (PMC) approach, or an auxiliary variable. Through extensive simulations, we confirmed that all the proposed techniques outperform the standard NBP method.