Model Selection in Historical Research Using Approximate Bayesian Computation.

FORMAL MODELS AND HISTORY: Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to re-evaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. CASE STUDY: This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester's laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. IMPACT: Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence.

Evidence of traditional knowledge loss among a contemporary indigenous society.

As biological and linguistic diversity, the world's cultural diversity is on decline. However, to date there are no estimates of the rate at which the specific cultural traits of a group disappear, mainly because we lack empirical data to assess how the cultural traits of a given population change over time. Here we estimate changes in cultural traits associated to the traditional knowledge of wild plant uses among an Amazonian indigenous society. We collected data among 1151 Tsimane' Amerindians at two periods of time. Results show that between 2000 and 2009, Tsimane' adults experienced a net decrease in the report of plant uses ranging from 9% (for the female subsample) to 26% (for the subsample of people living close to towns), equivalent to a 1 to 3 % per year. Results from a Monte Carlo simulation show that the observed changes were not the result of randomness. Changes were more acute for men than for women and for informants living in villages close to market towns than for informants settled in remote villages. The Tsimane' could be abandoning their traditional knowledge as they perceive that this form of knowledge do not equip them well to deal with the new socio-economic and cultural conditions they face nowadays.