Project Overview

Project Summary

The Project "The Evolution of Semantic Ambiguity in the Lab" (EvoSAL) is funded by the Polish National Agency for Academic Exchange (NAWA) through the Ulam Programme (call 2019) for a period of two years, starting December 2019. The principle investigator is Roland Mühlenbernd, the host affiliation is the Center for Language Evolution Studies (CLES) at the Nicolaus Copernicus University in Toruń, Poland. The EvoSAL project investigates the role of semantic ambiguity in human language from an evolutionary point of view. It draws on ideas and methods from linguistics, theoretical biology (part. Evolutionary Game Theory), experimental economics, cognitive science, computer science and philosophy.

Overall Objective

The research is supposed to shed new light on a key feature in human language: ambiguity. It can be shown that ambiguity does not only play an important role in human language, but also in animal communication (cf. Arnold & Zuberbühler 2006). The research is supposed to broadcast the novel idea that ambiguity is not a malus of communication (cf. Skyrms 2010), but it has very specific functions and it is under particular conditions more efficient than unambiguous communication systems (cf. Santana 2014). Understanding the functional aspect of ambiguity in language use and change will have an impact on all future investigations that study communication systems with respect to language evolution. The EvoSAL project will study ambiguity by calling attention to the role of cost-benefit aspects in language evolution and by applying particularly tools and methods from Evolutionary Game Theory and Experimental Economics.

Formal & Empirical Background

A simple, yet very popular, formal model for studying semantic systems is the signaling game (Lewis 1969), which models communication between a speaker and a hearer. Semantic meaning can be determined by strategies that depict encoding–decoding processes between information states and signals. By studying signaling games under evolutionary dynamics, it is possible to show that unambiguous signaling strategies are evolutionarily stable, whereas ambiguous strategies cannot survive under evolutionary dynamics (Wärneryd 1993), given basic conditions, such as aligned interests of interlocutors or no signal costs. However, it can be shown that particular conditions can make ambiguity evolutionarily stable (cf. Santana 2014, O'Connor 2015). The predictions about the emergence and stability of semantic systems in signaling games are mostly a result of formal analysis and simulation studies (cf. Huttegger 2007, Skyrms 2010). In recent years, a new research trend emerged that appeared to be the most promising way to explore and validate such formal predictions: the study of communication games with real humans in the lab, notably by applying tools from Experimental Semiotics (cf. Galantucci 2017) and Experimental Economics (cf. Bruner et al. 2019).

Research Steps & Tools

Only little work exists that studies the evolution of ambiguity in semantic systems (cf. Santana 2014, O'Connor 2015), and none of them uses an experimental approach. Thus, there is no empirical evidence for formally attested factors that support the evolution of ambiguity in semantic systems of human language. With EvoSAL I intend to fill this research gap by applying tools from Experimental Economics. The main goal is to investigate if humans actually develop the same patterns that are predicted by formal/computational studies. The work flow is as follows:

  • Step 1: Position paper about possible functions of semantic ambiguity in communication and its impact on language evolution. Working Hypothesis: Ambiguity plays an important role in language evolution.
  • Step 2: Formal and computational analysis of signaling games under evolutionary dynamics with the goal to detect conditions that support the emergence of semantic ambiguity. Working Hypothesis: Very particular factors support ambiguity.
  • Step 3: Conduction of laboratory experiments to validate the prediction of formal analyses by applying tools from Experimental Economics. A first experiment will check basic conditions, a second will check extended conditions detected in step 2. Working Hypothesis: Factors that support ambiguity can be validated.
These steps represent the work flow 'theory-> model -> experiment', which has become good practice in the field (cf. Chaudhry & Loewenstein 2019). To get a good idea of how the experiments will be implemented, I refer to Bruner et al. 2019.


  • Arnold & Zuberbühler (2006). Language evolution: semantic combinations in primate calls. Nature 441(7091): 303.
  • Bruner, O'Connor, Rubin (2019). Experimental Economics for Philosophers. In: E. Fischer, M. Curtis (eds.), Methodological Advances in Experimental Philosophy, Bloomsbury Academic.
  • Chaudhry & Loewenstein (2019). Thanking, apologizing, bragging, and blaming: Responsibility Exchange Theory and the currency of communication. Psychological Review 126(3), 313-344.
  • Galantucci (2017). Experimental Semiotics. Oxford Research Encyclopedia of Linguistics.
  • Huttegger (2007). Evolution and the explanation of meaning. Philosophy of Science 74, 1-27.
  • Lewis (1969). Convention. A Philosophical Study. Wiley-Blackwell.
  • O'Connor (2015). Ambiguity is kinda good sometimes. Philosophy of Science 82(1), 110-121.
  • Santana (2014). Ambiguity in cooperative signaling. Philosophy of Science 81(3), 398-422.
  • Skyrms (2010). Signals: Evolution, Learning and Information. OUP.
  • Wärneryd (1993). Cheap Talk, Coordination, and Evolutionary Stability. Games and Economic Behavior 5(4), 532-546.