This paper seeks to address the problem of the empirical identification of housing market segmentation, once we assume that submarkets exist. The typical difficulty in identifying housing submarkets when dealing with many locations is the vast number of potential solutions and, in such cases, the use of the Chow test for hedonic functions is not a practical solution. Here, we solve this problem by undertaking an identification process with a heuristic for spatially constrained clustering, the ''Housing Submarket Identifier'' (HouSI). The solution is applied to the housing market in the city of Barcelona (Spain), where we estimate a hedonic model for fifty thousand dwellings aggregated into ten groups. In order to determine the utility of the procedure we seek to verify whether the final solution provided by the heuristic is comparable with the division of the city into ten administrative districts.

Current models of decision-making more often than not ignore the level of difficulty of choices or treat it only informally. Yet, difficulty has been shown to affect human decision quality. We propose instance complexity (IC), a measure of computational resource requirements, as a generalisable framework to quantify difficulty of a choice based on a small number of properties of the choice. The main advantage of IC compared to other measures of difficulty is fourfold. Firstly, it is based on the theory of computation, a rigorous mathematical framework. Secondly, our measure captures complexity that is intrinsic to a decision task, that is, it does not depend on a particular solution strategy or algorithm. Thirdly, it does not require knowledge of a decision-maker's attitudes or preferences. And lastly, it allows computation of difficulty of a decision task ex-ante, that is, without solving the decision task. We tested the relation between IC and (i) decision quality and (ii) effort exerted in a decision using two variants of the 0-1 knapsack problem, a canonical and ubiquitous computational problem. We show that participants exerted more effort on instances with higher IC but that decision quality was lower in those instances. Together, our results suggest that IC can be used as a general framework to measure the inherent complexity of decision tasks and to quantify computational resource requirements of choices. The latter is particularly relevant for models of resource allocation in the brain (meta-decision-making/cognitive control). Our results also suggest that existing models of decision-making that are based on optimisation (rationality) as well as models such as the Bayesian Brain Hypothesis, are computationally implausible.Most theories of decision-making ignore the difficulty of making a decision [1][2][3]. They 2 assume that the decision-maker is always able to identify the best option-whether 3 it is a choice between two flavours of ice cream or a choice of investment option for 4 a retirement portfolio from thousands of available options. This is the case not only 5 for rational choice theories of decision-making [4-6], but also for theories of bounded 6 rationality [7-9] and theories of computational rationality [10,11]. All of those theories 7 assume, implicitly or explicitly, that an observed choice is the outcome of a (possibly 8 constrained) optimisation problem. 9Where decision difficulty has been taken into account, it has been done either 10 informally or in a highly domain-specific way. An example of the former are approaches 11 based on heuristics [12,13]. In this line of research, it is proposed that decision-makers 12 use simple rules or procedures as 'short cuts' to overcome various forms of cognitive 13 limitations. These approaches do not usually demonstrate, however, if and in what ways 14 the proposed heuristics overcome various cognitive limits. 15Other work on decision difficulty is domain-specific and cannot necessarily be gen-16 eralised. For example, it has been shown that the ab...

The survival of human organisms depends on our ability to solve complex tasks in the face of limited cognitive resources. However, little is known about the factors that drive the complexity of those tasks. Here, building on insights from computational complexity theory, we quantify the computational hardness of cognitive tasks using a set of task-independent metrics related to the computational resource requirements of individual instances of a task. We then examine the relation between those metrics and human behavior and find that they predict both time spent on a task as well as accuracy in three canonical cognitive tasks. Our findings demonstrate that performance in cognitive tasks can be predicted based on generic metrics of their inherent computational hardness.

No abstract

El presente artículo presenta el análisis realizado a las obras musicales seleccionadas de los compositores ocañeros homenajeados en el catálogo musical “Ocaña: legado de música, letras y pasiones”, el cual fue producto de la ejecución del proyecto “Compilación, análisis, clasificación y estudio de la obra musical de 4 compositores de Ocaña y la provincia; y elaboración de su catálogo musical”, con el objeto de rescatar la identidad musical y evitar la pérdida de la memoria cultural y musical de la región en peligro de desaparecer. El proyecto investigativo es de tipo cualitativo y el objeto de estudio fue la obra musical de los autores Rafael Contreras Navarro, Carlos Julio Melo Paredes, Carmen Noel Paba y Carlos Guillermo Lemus Sepúlveda. Como resultado del mismo, se realizó un análisis musical de las 2 obras de cada compositor que fueron seleccionadas, arregladas y producidas musicalmente en diferentes formatos instrumentales para el disco del catálogo. De este análisis se evidencia que en cada uno de ellos se siguen en términos generales los criterios de composición de la época reflejado en las métricas, los ritmos, los tonos, etc.