Exploring the “black box” of thermal adaptation using information entropy

Thermal adaptation has been interpreted well by behavioral, physiological, and psychological factors, but the mechanism and interaction between the three factors remain in the “black box”. This paper aims to apply the theory of general system and information entropy to investigate the quantitative relationships of the three thermal adaptation processes. Based on the database from the field survey and laboratory experiments conducted in the hot summer and cold winter climate zone of China, three typical adaptive indices: clothing insulation (Clo), thermal sensation votes (TSV) and sensory nerve conduction velocity (SCV) were selected to calculate Clo entropy, TSV entropy, SCV entropy and total entropy. The regression models were developed between these entropies and the indoor air temperature to quantify the weights of the three adaptive categories. The models were used to compare the differences between China and Pakistan as well as between adaptive approaches and climate chamber experiments. The thermal comfort and acceptable temperature ranges were obtained using the entropy models. Our findings propose a new perspective using entropy to quantify the behaviorally, physiologically, and psychologically adaptive approaches, which contribute to a better understanding of opening the “black box” of thermal adaptation

Exogenous cognition and cognitive state theory: the plexus of consumer analytics and decision-making

We develop the concept of exogenous cognition (ExC) as a specific manifestation of an external cognitive system (ECS). Exogenous cognition describes the technological and algorithmic extension of (and annexation of) cognition in a consumption context. ExC provides a framework to enhance understanding of the impact of pervasive computing and smart technology on consumer decision-making and the behavioural impacts of consumer analytics. To this end, the paper provides commentary and structures to outline the impact of ExC and to elaborate the definition and reach of ExC. The logic of ExC culminates in a theory of cognitive states comprising of three potential decision states; endogenous cognition (EnC), symbiotic cognition (SymC) and surrogate cognition (SurC). These states are posited as transient (consumers might move between them during a purchase episode) and determined by individual propensities and situational antecedents. The paper latterly provides various potential empirical avenues and issues for consideration and debate

Human migration: the big data perspective

How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants

Behavioral entropy and profitability in retail

Human behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer's behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatio-temporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products