Dates: from Jan. 1, 2017 to June 30, 2020

Funder: MINECO (Spain)

Project id: FIS2016-78904-C3-1-P

Total Funding: 211,000€

Node Funding: 121,000€

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StatMech2Pred is an ambitious project which intends to understand human behavior at individual and society scales by developing new prediction and observational techniques based on statistical mechanics. During the past 15 years we have witnessed a remarkable increase in both the scale and scope of social and behavioral data available. Such wealth of data has not only opened the possibilities to understand social systems in an unprecedented manner but also has also emphasized the need for new ways to observe, model and predict human behavior at micro and macro scales. From this perspective, tools built upon the principles of statistical mechanics are natural drivers to capture human-related phenomena.

Importantly, most of the recent advances in this area have been descriptive, and although many models have been proposed, the tools and data behind these models lack the accuracy to be predictive and prescriptive. Current models can neither anticipate the behavior of individuals nor the dynamics of the system as a whole. Therefore, to hypothesize about different scenarios is still an arduous task. For example, information diffusion is usually modelled by mechanistic models borrowed from biology, which do not describe the complex and context-dependent dynamics of information sharing. For models to become predictive we need: (i) better understanding of the micro-dynamics of human behavior and its non-trivial connection to contextual factors, (ii) better model selection and statistical inference tools, and (iii) better design of experiments and data collection. StatMech2Pred aims precisely at developing the complex systems tools necessary to infer predictive models of human behavior from empirical data in different contexts, and to carry out experiments to investigate and model aspects of human behavior that are not covered by existing datasets. Our cross-disciplinary approach will allow not only to have more accurate models of human behavior, but also to respond to important problems at macro level like financial markets stability, economic growth or social inequalities.

On the methodological side we will develop tools combining network and non-network inference and model-selection approaches, the theory of critical phenomena, and stochastic processes. Specifically, we will focus on the use of statistical mechanics to: (i) develop models of human micro-dynamics, and (ii) create better model selection tools from empirical data.

On the experimental side, we want to combine big data from online sources with data from experiments by for instance using social dilemmas. Through such experiments we will gather controlled data to either validate findings in data from online sources, or to answer specific fundamental questions related to human actions. Finally, we will analyze empirical data and develop predictive and grounded models considering the relationship between actions and contextual factors. Specifically, we will address: (i) the human decision making process in controlled settings, (ii) the impact of human guesses on market price changes, and (iii) the relationship between human behavior shifts and socio-economic indicators.

StatMech2Pred will draw from our previous experiences in the development of mathematical tools, data analysis and the setup of controlled experiments to go beyond the current understanding of human actions while creating tools and experimental frameworks to be used as references in future human behavior studies.

Publications

• Stochastic block models reveal a robust nested pattern in healthy human gut microbiomes - PNAS Nexus 1 (3) , pgac055 (2022).
• Node Metadata Can Produce Predictability Crossovers in Network Inference Problems - Phys. Rev. X 12 , 011010 (2022).
• Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies - EPJ Data Sci. 10 , 26 (2021).
• Predicting the photocurrent–composition dependence in organic solar cells - Energy Environ. Sci. 14 , 986 (2021).
• One model to rule them all in network science? - Proc. Natl. Acad. Sci. USA 117 (41) , 25195-25197 (2020).
• Reducing global environmental inequality: Determining regional quotas for environmental burdens through systems optimisation - Journal of Cleaner Production 270 , 121828 (2020).
• Bayesian machine scientist to compare data collapses for the Nikuradse dataset - Phys. Rev. Lett. 124 , 084503 (2020).
• A Bayesian machine scientist to aid in the solution of challenging scientific problems - Sci. Adv. 6 (5) , eaav6971 (2020).
• CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network - Bioinformatics 35 (20) , 4089 -4097 (2019).
• Tensorial and bipartite block models for link prediction in layered networks and temporal networks - Phys. Rev. E 99 , 032307 (2019).
• Optimal prediction of decisions and model selection in social dilemmas using block models - EPJ Data Sci. 7 , 48 (2018).
• Consistencies and inconsistencies between model selection and link prediction in networks - Phys. Rev. E 97 , 062316 (2018).
• Leader evaluation and team cohesiveness in the process of team development: A matter of gender? - PLoS ONE 12 (10) , e0186045 (2017).
• Bone fusion in normal and pathological development is constrained by the network architecture of the human skull - Sci. Rep. 7 , art. no. 3376 (2017).