Massimo Warglien

The emergence of simple languages in an experimental coordination game

We investigate in a series of laboratory experiments how costs and benefits of linguistic communication affect the emergence of simple languages in a coordination task when no common language is available in the beginning. The experiment involved pairwise computerized communication between 152 subjects involved in at least 60 rounds. The subjects had to develop a common code referring to items in varying lists of geometrical figures distinguished by up to three features. A code had to be made of a limited repertoire of letters. Using letters had a cost. We are interested in the question of whether a common code is developed, and what enhances its emergence. Furthermore, we explore the emergence of compositional, protogrammatical structure in such codes. We compare environments that differ in terms of available linguistic resources (number of letters available) and in terms of stability of the task environment (variability in the set of figures). Our experiments show that a too small repertoire of letters causes coordination failures. Cost efficiency and role asymmetry are important factors enhancing communicative success. In stable environments, grammars do not seem to matter much, and instead efficient arbitrary codes often do better. However, in an environment with novelty, compositional grammars offer considerable coordination advantages and therefore are more likely to arise.

Predicting Human Interactive Learning by Regret-Driven Neural Networks

Much of human learning in a social context has an interactive nature: What an individual learns is affected by what other individuals are learning at the same time. Games represent a widely accepted paradigm for representing interactive decision-making. We explored the potential value of neural networks for modeling and predicting human interactive learning in repeated games. We found that even very simple learning networks, driven by regret-based feedback, accurately predict observed human behavior in different experiments on 21 games with unique equilibria in mixed strategies. Introducing regret in the feedback dramatically improved the performance of the neural network. We show that regret-based models provide better predictions of learning than established economic models.

Using conceptual spaces to model actions and events

Actions and events are central to a semantics of natural language. In this article, we present a cognitively based model of these notions. After giving a general presentation of the theory of conceptual spaces, we explain how the analysis of perceptual concepts can be extended to actions and events. First, we argue that action space can be analyzed in the same way as, for example, colour space or shape space. Our hypothesis is that the categorization of actions depends, to a large extent, on the perception of forces. In line with this, we describe an action as a pattern of forces. We identify an action category as a convex region of action space. We review some indirect evidence for this representation. Second, we represent an event as an interaction between a force vector and a result vector. Typically an agent performs an action-that is, exerts a force-that changes the properties of the patient. Such a model of events is suitable for an analysis of the semantics of verbs. We compare the model to other related attempts from cognitive semantics. (Less)

Semantics, conceptual spaces, and the meeting of minds

We present an account of semantics that is not construed as a mapping of language to the world but rather as a mapping between individual meaning spaces. The meanings of linguistic entities are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modeled as convex regions in conceptual spaces. We outline a mathematical framework, based on fixpoints in continuous mappings between conceptual spaces, that can be used to model such a semantics. If concepts are convex, it will in general be possible for interactors to agree on joint meaning even if they start out from different representational spaces. Language is discrete, while mental representations tend to be continuous—posing a seeming paradox. We show that the convexity assumption allows us to address this problem. Using examples, we further show that our approach helps explain the semantic processes involved in the composition of expressions.

Cooperation, conceptual spaces and the evolution of semantics

We start by providing an evolutionary scenario for the emergence of semantics. It is argued that the evolution of anticipatory cognition and theory of mind in the hominids opened up for cooperation about future goals. This cooperation requires symbolic communication. The meanings of the symbols are established via a “meeting of minds.” The concepts in the minds of communicating individuals are modelled as convex regions in conceptual spaces. We then outline a mathematical framework based on fixpoints in continuous mappings between conceptual spaces that can be used to model such a semantics.

Object Salience in the Division of Labor: Experimental Evidence

When we engage in the process of division of labor, there are typically multiple alternatives, but insufficient knowledge to choose among them. Under such conditions, we propose that not all alternatives are equally likely to be pursued. In particular, when we engage in the process of division of labor for novel and nonrepetitive production, we argue that we display a tendency to perceive and select object-based task partitions over activity-based partitions. We experimentally investigate how the salience of objects over activities manifests itself in individuals and groups engaged in division of labor for the assembly of strongly or weakly decomposable products. We draw implications for organization design as well as the impact of technological change on organizations. This paper was accepted by Jesper Sorensen, organizations .

Constructing shared interpretations in a team of intelligent agents: the effects of communication intensity and structure

In this paper we explore a model of a team of intelligent agents constructing a shared interpretation of the state of their environment. Each agent is modeled as a constraint satisfaction network of the Hopfield (1982) type.

Neural network models of learning and categorization in multigame experiments.

Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2 × 2 games. Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium.

The Geometry of Negation

There are two natural ways of thinking about negation: (i) as a form of complementation and (ii) as an operation of reversal, or inversion (to deny that p is to say that things are “the other way around”). A variety of techniques exist to model conception (i), from Euler and Venn diagrams to Boolean algebras. Conception (ii), by contrast, has not been given comparable attention. In this note we outline a twofold geometric proposal, where the inversion metaphor is understoood as involving a rotation or a reflection, respectively. These two options are equivalent in classical two-valued logic but they differ significantly in many-valued logics. Here we show that they correspond to two basic sorts of negation operators— Posts and Kleenes—and we provide a simple group-theoretic argument demonstrating their generative power.

Garbage Can in the Lab

We develop an experimental setting where the assumptions and predictions of the garbage can model can be tested. A careful reconstruction of the original simulation model enables us to select parameters that leave room for potential variations in individual behavior. Our experimental design replicates these parameters and thereby facilitates comparison of human behavior with the behavioral assumption of the original model. We find that the majority strategy of human subjects is consistent with the original model, but exhibits some extent of behavioral diversity. What is notable is that, although slightly outperforming the simulation model, human subjects failed to learn at the group level to improve performance. This is because human subjects exhibit fluid diverse behaviors that improve coordination in the face of uncertainty, but hinder collective learning.