Workplace duties or opportunities? Challenge stressors, regulatory focus, and creativity

Previous research has produced contradictory findings about the impact of challenge stressors on individual and team creativity. Based on the challenge-hindrance stressors framework (LePine, Podsakoff, & LePine, 2005) and on regulatory focus theory (Higgins, 1997), we argue that the effect of challenge stressors on creativity is moderated by regulatory focus. We hypothesize that while promotion focus strengthens a positive relationship between challenge stressors and creativity, prevention focus reinforces a negative relationship. Experimental data showed that high demands led to better results in a creative insight task for individuals with a strong trait promotion focus, and that high demands combined with an induced promotion focus led to better results across both creative generation and insight tasks. These results were replicated in a field R&D sample. Furthermore, we found that team promotion focus moderated the effect of challenge stressors on team creativity. The results offer both theoretical insights and suggest practical implications. © 2013 Elsevier Inc.

Diversity in goal-orientation, team reflexivity, and team performance

Although recent research highlights the role of team member goalorientation in team functioning, research has neglected the effects of diversity in goalorientation. In a laboratory study with groups working on a problem-solving task, we show that diversity in learning and performanceorientation are related to decreased group performance. Moreover, we find that the effect of diversity in learning orientation is mediated by group information elaboration and the effect of diversity in performanceorientation by group efficiency. In addition, we demonstrate that teamreflexivity can counteract the negative effects of diversity in goalorientation. These results suggest that models of goal orientation in groups should incorporate the effects of diversity in goal orientation.

Sensitivity and Bias within the Binary Signal Detection Theory, BSDT

Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.

An intelligent broker agent for energy trading:an MDP approach

This paper details the development and evaluation of AstonTAC, an energy broker that successfully participated in the 2012 Power Trading Agent Competition (Power TAC). AstonTAC buys electrical energy from the wholesale market and sells it in the retail market. The main focus of the paper is on the broker’s bidding strategy in the wholesale market. In particular, it employs Markov Decision Processes (MDP) to purchase energy at low prices in a day-ahead power wholesale market, and keeps energy supply and demand balanced. Moreover, we explain how the agent uses Non-Homogeneous Hidden Markov Model (NHHMM) to forecast energy demand and price. An evaluation and analysis of the 2012 Power TAC finals show that AstonTAC is the only agent that can buy energy at low price in the wholesale market and keep energy imbalance low.

Specification and quantitative analysis of probabilistic cloud deployment patterns

Cloud computing is a new technological paradigm offering computing infrastructure, software and platforms as a pay-as-you-go, subscription-based service. Many potential customers of cloud services require essential cost assessments to be undertaken before transitioning to the cloud. Current assessment techniques are imprecise as they rely on simplified specifications of resource requirements that fail to account for probabilistic variations in usage. In this paper, we address these problems and propose a new probabilistic pattern modelling (PPM) approach to cloud costing and resource usage verification. Our approach is based on a concise expression of probabilistic resource usage patterns translated to Markov decision processes (MDPs). Key costing and usage queries are identified and expressed in a probabilistic variant of temporal logic and calculated to a high degree of precision using quantitative verification techniques. The PPM cost assessment approach has been implemented as a Java library and validated with a case study and scalability experiments. © 2012 Springer-Verlag Berlin Heidelberg.

Hálózati kísérletek (Network experiments)

A társadalmi kapcsolatok hálózatának elemzése akárcsak a kísérleti közgazdaságtan, az utóbbi évtizedben rohamosan fejlődő diszciplína, amely számos új tudományos eredménnyel gazdagította és gazdagítja a társadalmi és gazdasági jelenségek magyarázatait. A hálózati kísérletek a két diszciplína találkozási pontján különösen tanulságosak, hiszen nem pusztán a hálózati iparágak működését és problémáit segítenek megérteni és megmagyarázni, de eredményeik meghatározó jelentőségűek a fogyasztói döntéshozatal vizsgálatában és a piaci verseny szabályozásának szempontjából is. A jelen tanulmány áttekintést ad a hálózati kísérletekről, ezen belül a lokális interakciós játékokról, a strukturálisan beágyazott játékokról, a cserehálózatok irodalmáról és a hálózatformálódási játékokról, valamint bemutatja a legfontosabb eredmények gyakorlati alkalmazási lehetőségeit és az alkalmazhatóság korlátait. / === / Analysis of the network of social relations, like experimental economics as a whole, is a discipline that has developed rapidly in recent decades and provided many new findings to explain societal and economic phenomena. Network experiments are notably instructive at the point of contact between the two disciplines, for besides helping to comprehend and explain the operation and problems of the network industries, they produce findings of decisive importance to examining consumer decision-making and regulating market competition. The study looks into network experiments, including local interaction games, structurally grounded games, the literature on exchange networks, and network-shaping games, as well as presenting the main results of applying them and the restrictions on doing so.

Mother-child interactions in youth purchase decisions

This study examines the impact of mother-child interactions on youth purchase decisions with a clear focus on dependent young adults living in the parental home. Two studies were carried out using both quantitative and qualitative approaches in order to understand the characteristics of young adults’ purchase decision-making. In the first study, a survey was distributed among young adults, and in the second study, several short essays from pairs of young adults and their mothers were analysed. Findings suggest that mother-child communication has a significant impact on children’s consumer decision-making style. Furthermore, these results draw particular attention to the laissez-faire communication style, which is relevant due to both its prevalence and its influence on youth decision-making. We also conclude that the product or service category is a critical consideration when the independence of young adults is evaluated in relation to their purchases.

Visitor At-Destination Search for Travel-Related Services

The phenomenon of at-destination search activity and decision processes utilized by visitors to a location is predominantly an academic unknown. As destinations and organizations increasingly compete for their share of the travel dollar, it is evident that more research need to be done regarding how consumers obtain information once they arrive at a destination. This study examined visitor referral recommendations provided by hotel and non-hotel ''locals" in a moderately-sized community for lodging, food service, and recreational and entertainment venues.

A simulation-based heuristic for fleet assignment

Integer programming, simulation, and rules of thumb have been integrated to develop a simulation-based heuristic for short-term assignment of fleet in the car rental industry. It generates a plan for car movements, and a set of booking limits to produce high revenue for a given planning horizon. Three different scenarios were used to validate the heuristic. The heuristic's mean revenue was significant higher than the historical ones, in all three scenarios. Time to run the heuristic for each experiment was within the time limits of three hours set for the decision making process even though it is not fully automated. These findings demonstrated that the heuristic provides better plans (plans that yield higher profit) for the dynamic allocation of fleet than the historical decision processes. Another contribution of this effort is the integration of IP and rules of thumb to search for better performance under stochastic conditions.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Estratégias para a organização da pesquisa em cana-de-açúcar: uma análise de governança em sistemas de inovação

PEDRO, Edilson da Silva. Estratégias para a organização da pesquisa em cana-de-açúcar: uma análise de governança em sistemas de inovação. 2008. 226f. Tese (Doutorado em Política Científica e Tecnológica) - Universidade Estadual de Campinas, Campinas, 2008.

Estratégias para a organização da pesquisa em cana-de-açúcar: uma análise de governança em sistemas de inovação

PEDRO, Edilson da Silva. Estratégias para a organização da pesquisa em cana-de-açúcar: uma análise de governança em sistemas de inovação. 2008. 226f. Tese (Doutorado em Política Científica e Tecnológica) - Universidade Estadual de Campinas, Campinas, 2008.

Exact Solution of Bayes and Minimax Change-Detection Problems

The challenge of detecting a change in the distribution of data is a sequential decision problem that is relevant to many engineering solutions, including quality control and machine and process monitoring. This dissertation develops techniques for exact solution of change-detection problems with discrete time and discrete observations. Change-detection problems are classified as Bayes or minimax based on the availability of information on the change-time distribution. A Bayes optimal solution uses prior information about the distribution of the change time to minimize the expected cost, whereas a minimax optimal solution minimizes the cost under the worst-case change-time distribution. Both types of problems are addressed. The most important result of the dissertation is the development of a polynomial-time algorithm for the solution of important classes of Markov Bayes change-detection problems. Existing techniques for epsilon-exact solution of partially observable Markov decision processes have complexity exponential in the number of observation symbols. A new algorithm, called constellation induction, exploits the concavity and Lipschitz continuity of the value function, and has complexity polynomial in the number of observation symbols. It is shown that change-detection problems with a geometric change-time distribution and identically- and independently-distributed observations before and after the change are solvable in polynomial time. Also, change-detection problems on hidden Markov models with a fixed number of recurrent states are solvable in polynomial time. A detailed implementation and analysis of the constellation-induction algorithm are provided. Exact solution methods are also established for several types of minimax change-detection problems. Finite-horizon problems with arbitrary observation distributions are modeled as extensive-form games and solved using linear programs. Infinite-horizon problems with linear penalty for detection delay and identically- and independently-distributed observations can be solved in polynomial time via epsilon-optimal parameterization of a cumulative-sum procedure. Finally, the properties of policies for change-detection problems are described and analyzed. Simple classes of formal languages are shown to be sufficient for epsilon-exact solution of change-detection problems, and methods for finding minimally sized policy representations are described.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Urban Agriculture Oriented towards Self-Supply, Social and Commercial Purpose: A Typology

Urban agriculture, a dynamic multifunctional phenomenon, affects the spatial diversification of urban land use, its valorization and its governance. Literature acknowledges its contribution to the development of sustainable cities. The dimension and extent of this contribution depends significantly on the particular form and function of urban agriculture. However, the complexity of interests and dimensions is insufficiently covered by theory. This paper proposes a typology for urban agriculture, supporting both theory building and practical decision processes. We reviewed and mapped the diversity of the types of agriculture found along three beneficial dimensions (self-supply, socio-cultural, commercial) for product distribution scale and actors. We distinguish between ideal types, subtypes and mixed types. Our intention is to include a dynamic perspective in the typology of urban agricultural land use because transition processes between types are observable due to the existence of complex motivations and influences. In a pilot study of 52 urban agriculture initiatives in Germany, we tested the validity of the typology and discussed it with stakeholders, proving novelty and relevance for profiling discussions.