Spectral Decompositions of Multiple Time Series: A Bayesian Non-parametric Approach

We consider spectral decompositions of multiple time series that arise in studies where the interest lies in assessing the influence of two or more factors. We write the spectral density of each time series as a sum of the spectral densities associated to the different levels of the factors. We then use Whittle’s approximation to the likelihood function and follow a Bayesian non-parametric approach to obtain posterior inference on the spectral densities based on Bernstein–Dirichlet prior distributions. The prior is strategically important as it carries identifiability conditions for the models and allows us to quantify our degree of confidence in such conditions. A Markov chain Monte Carlo (MCMC) algorithm for posterior inference within this class of frequency-domain models is presented. We illustrate the approach by analyzing simulated and real data via spectral one-way and two-way models. In particular, we present an analysis of functional magnetic resonance imaging (fMRI) brain responses measured in individuals who participated in a designed experiment to study pain perception in humans.     

Structural Inference from Conditional Knowledge Bases

There are several approaches implementing reasoning based on conditional knowledge bases, one of the most popular being System Z (Pearl, Proceedings of the 3rd conference on theoretical aspects of reasoning about knowledge, TARK ’90, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 121–135, 1990). We look at ranking functions (Spohn, The Laws of Belief: Ranking Theory and Its Philosophical Applications, Oxford University Press, Oxford, 2012) in general, conditional structures and c-representations (Kern-Isberner, Conditionals in Nonmonotonic Reasoning and Belief Revision: Considering Conditionals as Agents, vol. 2087 of LNCS, Springer, Berlin, 2001) in order to examine the reasoning strength of the different approaches by learning which of the known calculi of nonmonotonic reasoning (System P and R) and Direct Inference are applicable to these inference relations. Furthermore we use the recently proposed Enforcement-postulate (Kern-Isberner and Krümpelmann, Proceedings of the 22nd international joint conference on artificial intelligence, vol. 2, IJCAI’11, AAAI Press, pp. 937–942, 2011) to show dependencies between these approaches.     

Probability Propagation in Generalized Inference Forms

Probabilistic inference forms lead from point probabilities of the premises to interval probabilities of the conclusion. The probabilistic version of Modus Ponens, for example, licenses the inference from \({P(A) = \alpha}\) and \({P(B|A) = \beta}\) to \({P(B)\in [\alpha\beta, \alpha\beta + 1 - \alpha]}\) . We study generalized inference forms with three or more premises. The generalized Modus Ponens, for example, leads from \({P(A_{1}) = \alpha_{1}, \ldots, P(A_{n})= \alpha_{n}}\) and \({P(B|A_{1} \wedge \cdots \wedge A_{n}) = \beta}\) to an according interval for P(B). We present the probability intervals for the conclusions of the generalized versions of Cut, Cautious Monotonicity, Modus Tollens, Bayes’ Theorem, and some SYSTEM O rules. Recently, Gilio has shown that generalized inference forms “degrade”—more premises lead to less precise conclusions, i.e., to wider probability intervals of the conclusion. We also study Adam’s probability preservation properties in generalized inference forms. Special attention is devoted to zero probabilities of the conditioning events. These zero probabilities often lead to different intervals in the coherence and the Kolmogorov approach.     

Structuring for team success: The interactive effects of network structure and cultural diversity on team potency and performance

This longitudinal study used data from 91 self-managed teams (456 individuals, 60 nationalities) to examine the interactive effects of a team’s task (“workflow”) network structure and its cultural diversity (as indexed by nationality) on the team’s “potency” (i.e., the team’s confidence in its ability to perform) and its performance (as rated by expert judges). We found that whereas the emergence of dense task networks enhanced team potency it was the emergence of (moderately) centralized task networks that facilitated team performance. These varied structural effects, moreover, were themselves contingent on team composition: the more culturally diverse a team, the more pronounced were the positive effects of network density on team potency and the higher the level of network centralization required for optimal team performance. The success of a team appears to hinge on the interplay between network structure and team composition.     

A Description–Experience Gap in Social Interactions: Information about Interdependence and Its Effects on Cooperation

In social interactions, decision makers are often unaware of their interdependence with others, precluding the realization of shared long‐term benefits. In an experiment, pairs of participants played an Iterated Prisoner's Dilemma under various conditions involving differing levels of interdependence information. Each pair was assigned to one of four conditions: “No‐Info” players saw their own actions and outcomes, but were not told that they interacted with another person; “Min‐Info” players knew they interacted with another person but still without seeing the other's actions or outcomes; “Mid‐Info” players discovered the other's actions and outcomes as they were revealed over time; and “Max‐Info” players were also shown a complete payoff matrix mapping actions to outcomes from the outset and throughout the game. With higher levels of interdependence information, we found increased individual cooperation and mutual cooperation, driven by increased reciprocating cooperation (in response to a counterpart's cooperation). Furthermore, joint performance and satisfaction were higher for pairs with more information. We discuss how awareness of interdependence may encourage cooperative behavior in real‐world interactions. Copyright © 2013 John Wiley & Sons, Ltd.     

Meanings of “the existential” in a Secular Country: A Survey Study

“The existential” is a concept that many people use albeit associated with different meanings. In order to increase research-based insight into the meaning of “the existential,” we conducted a questionnaire study in Denmark in 2018 in which we asked 1.106 Danes of various age, gender, educational and geographical background about personal associations linked to “the existential.” Factor analysis of the answers resulted in three different groups of meaning: (1) essential meanings of life, (2) spirituality/religiosity and (3) existential thinking. The findings show that “the existential” serves well as an overarching construct potentially including secular, spiritual and religious meaning domains, at least within the European context.

     

Measuring Spiritual Needs in a Secular Society: Validation and Clinimetric Properties of the Danish 20-Item Spiritual Needs Questionnaire

Journal of Religion and Health - In secular cultures, such as Denmark, tools to measure spiritual needs are warranted to guide existential and spiritual care. We examined the clinimetric properties...     

Socialism Order of Worth and Analytical Adequacy Axiom

Human Studies - Boltanski and Thévenot (On justification. Economies of worth, Princeton University Press, Princeton, 2006) constructed in their seminal work On Justification the Orders of...     

Collective fit increases team performances: extending regulatory fit from individuals to dyadic teams

People experience “regulatory fit” when they pursue a goal in a manner that suits their chronic regulatory orientation. This regulatory fit impacts performance positively. The present research extends performance gains due to fit from individuals to dyadic team performance. Study 1 manipulated team fit of 32 table football participants (i.e., promotion vs. prevention orientation and offense vs. defense positions). Team fit significantly predicted team success in an experimental tournament beyond team skill level. Study 2 replicated this result with data from a real‐life tournament including 66 highly experienced competitors. These findings broaden the concept of regulatory fit from individual to dyadic teams, and suggest collective fit as a possible important predictor for team success.