Papers from the International Association for Cross-Cultural Psychology Conferences

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As almost every major problem that humankind faces is a consequence of human behaviour, predicting behaviour and behaviour change is fundamental. Given the multitude of factors that affect our decision making, a transdisciplinary understanding of behaviour is impossible without the integration of data that crosses disciplinary boundaries. The concept of Conduct-“ome” is an analog of those holistic –“omic”-approaches found in the biological sciences which take a “totality of factors” approach, and provides a framework for studying human behaviour in a multifactorial, multidisciplinary context, accounting for a wealth of potential causes of behaviour, from the genetic and epigenetic to psychological, neurological, social, physiological, clinical, socio-economic, socio-demographic, socio-political and ethical factors. Conductome, as opposed to behaviour-ome, is used, as it directly addresses the “whys” (causes) of the considered behaviour. We argue that behaviour can only be understood probabilistically, through a process of statistical inference that constructs P(A|X), the probability for a conduct A conditioned on the large set of factors, X, that predict it. This inference process can be based on an “external” ensemble of objective, countable events, using a frequentist interpretation of probabilities, or on an “internal” ensemble, implicit in our mental models and based on a Bayesian interpretation. Including both these approaches allows one to compare objective, observable reality with the subjective perception of reality constructed within a mental model, allowing for the identification of discrepancies between the two in the form of cognitive biases. A key component for constructing the Conductome is the obtention of data that transcends disciplines that can be used to link a range of relevant behaviours as internal and external effects to their causes. A second component is the use of advanced modelling tools, such as machine learning, for the analysis of such multi-scale data and the construction of explicit prediction models for a given conduct. In this article, the feasibility of the Conductome approach is illustrated by considering obesity-related behaviours; as obesity has become one of the key social problems that affects a growing segment of the population worldwide. In summary: The objective is to understand, interpret and provide an interdisciplinary, computational, and data-based framework for generating prediction models for addressing problems that originate in human behaviour.


This work was supported in part by PAPIIT-DGAPA grant IG101520 and a donation from Microsoft Academic Relations.

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