Coordination, focal points and voting in strategic situations : a natural experiment

This paper studies coordination in a multi-stage elimination tournament with large monetary incentives and a diversified subject pool drawn from the adult British population. In the tournament, members of an ad hoc team earn money by answering general knowledge questions and then eliminate one contestant by plurality voting without prior communication. We find that in the early rounds of the tournament, contestants use a focal principle and coordinate on one of the multiple Nash equilibria in pure strategies by eliminating the weakest member of the team. However, in the later rounds, contestants switch to playing a mixed strategy Nash equilibrium

Servitization through human-data interaction : a behavioural approach

Purpose: This paper proposes a new approach to servitization and business models by understanding behavioural aspects of human interactions with technology, specifically, with “smart” devices, connected devices, autonomous systems, and internet of things (IoT) through understanding and interacting with data which these devices and systems generate. Design/methodology/approach: Proposed approach, Behavioural Human Data Interaction Hypothesis (Behavioural HDI Hypothesis), which differs from existing literature, leverages on research in behavioural science, data-driven business models, multi-sided markets, and Human-Data Interaction (HDI). Findings: Behavioural HDI Hypothesis can offer a new approach to future markets for data because it helps to (a) predict consumer choice of product and services; (b) suggest new and improved interaction mechanisms between consumers and their self-generated data; and (c) propose a new approach for building and evaluating business models. Originality/value: To date, very little has been known about whether and how consumers and households accumulate, review and use self-generated data about consumption decisions and how this affects market relationships between consumers and providers of goods and services. This paper shows how Behavioural HDI Hypothesis can make markets for data more efficient through better personalisation and servitization. It also has implications for data collection visibility, data ownership and structure, platform trade-off, security and other ICT-related challenges which negatively affect current business models in the digital economy

Endowment effects? "Even” with half a million on the table!

In the television show Deal or No Deal, a contestant is endowed with a sealed box containing a monetary prize between one cent and half a million euros. In the course of the show, the contestant is offered to exchange her box for another sealed box with the same distribution of possible monetary prizes inside. This offers a unique natural experiment for studying endowment effects under high monetary incentives. We find evidence of only a weak endowment effect when contestants exchange their box for another box with the same distribution of possible prize

Risk Aversion when Gains are Likely and Unlikely: Evidence from a Natural Experiment with Large Stakes

In the television show Deal or No Deal a contestant is endowed with a sealed box, which potentially contains a large monetary prize. In the course of the show the contestant learns more information about the distribution of possible monetary prizes inside her box. Consider two groups of contestants, who learned that the chances of their boxes containing a large prize are 20% and 80% correspondingly. Contestants in both groups receive qualitatively similar price offers for selling the content of their boxes. If contestants are less risk averse when facing unlikely gains, the price offer is likely to be more frequently rejected in the first group than in the second group. However, the fraction of rejections is virtually identical across two groups. Thus, contestants appear to have identical risk attitudes over (large) gains of low and high probabilit

Fluence dependent femtosecond quasi-particle and Eu^{2+} -spin relaxation dynamics in EuFe_{2}(As,P)_{2}

We investigated temperature and fluence dependent dynamics of the time resolved optical reflectivity in undoped spin-density-wave (SDW) and doped superconducting (SC) EuFe$_{2}$(As,P)$_{2}$ with emphasis on the ordered Eu$^{2+}$-spin temperature region. The data indicate that the SDW order coexists at low temperature with the SC and Eu$^{2+}$-ferromagnetic order. Increasing the excitation fluence leads to a thermal suppression of the Eu$^{2+}$-spin order due to the crystal-lattice heating while the SDW order is suppressed nonthermally at a higher fluence

Linking Physics and Psychology of Bistable Perception Using an Eye Blink Inspired Quantum Harmonic Oscillator Model

This paper introduces a novel quantum-mechanical model that describes psychological phenomena using the analogy of a harmonic oscillator represented by an electron trapped in a potential well. Study~1 demonstrates the application of the proposed model to bistable perception of ambiguous figures (i.e., optical illusions), exemplified by the Necker cube. While prior research has theoretically linked quantum mechanics to psychological phenomena, in Study~2 we demonstrate a viable physiological connection between physics and bistable perception. To that end, the model draws parallels between quantum tunneling of an electron through a potential energy barrier and an eye blink, an action known to trigger perceptual reversals. Finally, we discuss the ability of the model to capture diverse optical illusions and other psychological phenomena, including cognitive dissonance

Clustering big urban data sets

Cities are producing and collecting massive amount of data from various sources such as transportation network, energy sector, smart homes, tax records, surveys, LIDAR data, mobile phones sensors etc. All of the aforementioned data, when connected via the Internet, fall under the Internet of Things (IoT) category. To use such a large volume of data for potential scientific computing benefits, it is important to store and analyze such amount of urban data using efficient computing resources and algorithms. However, this can be problematic due to many challenges. This article explores some of these challenges and test the performance of two partitional algorithms for clustering Big Urban Datasets, namely: the K-Means vs. the Fuzzy cMean (FCM). Clustering Big Urban Data in compact format represents the information of the whole data and this can benefit researchers to deal with this reorganized data much efficiently. Our experiments conclude that FCM outperformed the K-Means when presented with such type of dataset, however the later is lighter on the hardware utilisations