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The Parametric Comparison Method – PCM is a research line which combines the goal of reconstructing language history, pursued since the rise of the classical comparative method in the 19th century, with the analytical tools of formal grammar and cognitive science developed in the last part of the 20th century.

Since its beginning (Longobardi 2003; Guardiano and Longobardi 2005, 2017; Longobardi and Guardiano 2009), the basic hypothesis of the PCM is that, contrary to most claims over the past two centuries, syntactic diversity encodes language history to a remarkable extent. 

Thus, the PCM explores the possibility of historical classification of languages using generative syntactic parameters as comparanda alongside and beyond word etymologies.

The fundamental hypothesis has been increasingly corroborated: the distribution of parametric distances is informative and not random (cf. Guardiano and Longobardi’s 2005 Anti‐Babelic Principle, and the algorithms developed in Bortolussi et al. 2011); their aggregations retrieve almost all independently established genealogical clusters (Longobardi et al. 2013, Guardiano et al. 2016, Ceolin et al. 2020) and suggest new, deeper relations that can be statistically assessed through explicit methods (Ceolin et al. 2021).


The PCM consists of the following components:

1) a set of parameters for a single module of grammar.

The modularized approach, first described in Longobardi (2003), is based on the assumption that the investigation of a single module of grammar, rather than a scattered set of parameters/properties, allows one to attain both depth of analysis and sufficient cross‐linguistic coverage. It consists of the two following strategies:

a. focusing on a predefined subset of parameters that contain only limited perturbation by the states of others outside the subset, but carefully identified interactions within the subset.

b. starting from a sample of languages and families that comprise a core of structurally similar varieties exhibiting salient minimal contrasts.

The module we selected to develop our Method is the internal syntax of Determiner Phrases (DPs).

Here you find an updated list of parameters used in the most recent PCM experiments (Ceolin et al. 2021).

2) a set of implicational formulas defining the interdependencies among parameters.

Parameters form a network of partial implications (Baker 2001, Longobardi and Guardiano 2009, Roberts 2019). The PCM modularized strategy has revealed that these interdependencies are pervasive (Guardiano and Longobardi 2017). As a consequence, several parameter values (around 40% in our datasets) become fully predictable, and represent redundant information which must be disregarded for phylogenetic comparison.

3) a procedure for setting parameter values using the empirical evidence available to the learner.

The most recent description of the parameter setting algorithm we adopt can be found in Crisma et al. (2020). Each parameter is associated with a set of simple existential questions, each of the type “Does a (set of) structure(s)/interpretation(s) so-and-so occur in language L?”. The answer YES can be assumed on the basis of positive evidence only.

4) a procedure to extract the historical signal from the parameter values through a set of quantitative algorithms


The measurement of the degree of similarity between remote languages is one of the major assets of the PCM: it sharply distinguishes the PCM from the classical comparative method and allows it to go beyond the limits of families identified by means of lexical etymologies.

This potential globality and time depth of the PCM has been pursued after the model of genetic anthropology, as established by Luca Cavalli Sforza and many of his followers and collaborators in the past few decades.

On these grounds, the PCM could be applied to the investigation of time-deep history in collaboration with other disciplines, giving rise to explorations of gene-language comparison (Colonna et al. 2010, Longobardi et al. 2015, Santos et al. 2020).

On the whole, the PCM has already begun to provide insights relevant for further neighboring fields: the analysis of ultralocality and  parameter microvariation (Guardiano et al. 2016), and the modeling of parameter diversity (Longobardi 2014, 2017, Crisma and Longobardi 2020).

For a recent description of the method and its history, see Guardiano et al. (2020).