pragmatic_agents_me_bias

This code belongs to the following paper:

Ohmer, X., König, P., Franke, M. (2020). Reinforcement of semantic representations in pragmatic agents leads to the emergence of a mutual exclusivity bias. 42nd Annual Meeting of the Cognitive Science Society, pp. 1779-1785.

1. Notation

In code and file names 'labeling' refers to the single agent setting of the Lewis game and 'communication' refers to the two agent setting (see paper).

2. Prerequisites

The project was implemented with Python 3.7.3. Apart from standard library packages we use:

• tensorflow 2.0 and numpy 1.16.4 (for implementing the agents and running the simulations)

• matplotlib 3.1.0 (for plotting the data)

While tensorflow (>=)2.0 is required other versions for python and numpy may work.

3. Files

The project contains

• the file RSA_communication_agents.py, where the agents are implemented

• the folder ME_bias for Experiment 1

• the folder convergence_time_scales for Experiment 2

We describe the two folders in more detail below.

ME_bias

This folder contains all code belonging to Experiment 1, which investigates the emergence of a mutual exclusivity bias in literal and pragmatic agents.

• ME_bias_labeling.ipynb: runs the simulations and saves the data for the single agent setting

• ME_bias_communication.ipynb: runs the simulations and saves the data for the two agent setting

• plot_ME_bias_labeling.ipynb: visualizes the data from the single agent setting

• plot_ME_bias_communication.ipynb: visualizes the data from the two agent setting

ME_data_analysis_and_visualization_functions.py: contains the functions for plotting the data and calculating the ME index

Apart from minor changes the plots correspond to the figures in the paper. Note that in our implementation the first and the last input (by index) are withheld from training. In the paper we report that the last two inputs are withheld which requires rearrangement for the plots.

convergence_time_scales

This folder contains all code belonging to Experiment 2, which investigates the convergence time scales of literal and pragmatic agents for different types of input distributions.

• convergence_time_labeling.ipynb: runs the simulations and saves the data for the single agent setting

• convergence_time_communication.ipynb: runs the simulations and saves the data for the two agent setting

• plot_convergence_time_scales.ipynb: plots the data from both settings

Also here, apart from minor changes the plots correspond to the figures in the paper.

4. Practical information

Input data

Input states and messages are encoded as one-hot vectors. To avoid indexing matrix rows and columns when accessing the lexicon, these one-hot vectors are mapped onto matrices with the corresponding row or column being equal to one. In order to extract a matrix column/row we can multiply such a matrix with the lexicon and collapse the product along one axis. This way we can use batch-wise training.

Numerical instabilities

When applying the model to larger systems or when using extreme values for the alpha parameter numerical instabilities can arise. This can be fixed by minor changes to the agents' implementations. Simply add a very small epsilon value (e.g. 10^-10) to prevent dividing by zero or taking the logarithm of zero. Make sure that epsilon is small enough so it does not interfere with the calculation. A side note on that: if you add epsilon to avoid taking the logarithm of zero in the sampling function, do so already on the return value of the call function to allow for correct computation of the gradient. We did not include such a fix as it is not required for the simulations in the paper.