Chilam – Laboratory for the Simulation of Complex Adaptive Systems

Every major problem that humankind faces can be linked to the consequences of human behaviour. As well as obesity, chronic and other lifestyle diseases – addictions, loss of biodiversity, climate change, pollution, violent conflict, fake news and others, all arise from how we conduct ourselves in our decision making, at both the individual and collective levels. The universality of these global problems, that are a direct result of our behaviour, demonstrates that underpinning these problems there exist, despite its complexity and richness, quite universal tendencies in human behaviour that are produced by biases that orient us to act in certain fixed directions, often with adverse consequences.

Given the multitude of factors that affect our decision making, a transdisciplinary understanding of behaviour is impossible without the integration of data that cross 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.

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, and which can be used to link a range of relevant behaviours, as effects, to their causes, both internal and external. 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.

The current emphasis of the Conductome project is on obesity and metabolic disease. Obesity encompasses risk factors from the genetic to the economic and political. However, deep down, it is a consequence of unhealthy behavior over long periods. Our behavior in turn is affected by the obesogenic environment in which we live and by our “mental model” of the world and of ourselves.
Since 2014, we have collected data on over 7000 individuals in Mexico, ranging from genetic data to psychological profiles and detailed information about daily habits and environment. This data is currently being cleaned and curated to be made widely available to the community of researchers, students and medical professionals who are interested in using this data to better understand and reduce the impact of these diseases.

To learn more about Conductome, consult the chapter dedicated to this Project in the book “Obesity and metabolism, an integrative perspective: contributions from basic science to applied science” (2022) Editorial Manual Moderno. (to be published). See Chapter