Date: 2025-01-13
Designer: Hiro KATAOKA (University of Tsukuba)
Hypotheses: Figure 4 in Sasahara et al. (2020, doi: 10.1007/s42001-020-00084-7) can be reproduced
100 agents; 1 runs; 500 games
fixed variables: WORKFLOW TOPIC ATOM DELTA PACTIVE PREWRITE NBITERATIONS NBAGENTS NBRUNS ALPHA MU REWRITE PREHOC
controled variables: EPSILONS SEEDS INITS VALUES
dependent variables: mdo ngo ano
Before verifing each hypotheses, we define the measures needed to check them. Let $A$ be the set of agents.
This measure shows the maximal distance between opinions: $$ \max_{a,a'\in A}|O_a-O_{a'}| $$ or equivalently, $$ \max_a O_a - \min_{a'} O_{a'}. $$
Intuitively, the more mdo is, the more polarized agents' opinions are.
This measure shows the number of groups of agents based on opinions.
The $i$-th group $G_i$ is a nonempty subset of $A$ such that $G_i\cap G_j=\emptyset$ if $i\neq j$. Each $G_i$ can be defined by performing hierarchial clustering with complete linkage: two clusters $C$ and $C'$ are merged iff $$\max_{a\in C,a'\in C'}|O_a-O_{a'}|\leq 0.1.$$
Intuitively, the more ngo is, the more polarized agents' opinions are.
This measure shows the average number of neighbors in another communities. Let $G_1,\ldots,G_n$ be the groups based on opinions. Let $E(\subseteq A\times A)$ be the set of edges in the network agents form. Then, this measure is defined as follows: $$ \frac{1}{n}|\{(a,a')\in E; \exists ij.a\in G_i,a'\in G_j,i\neq j\}|. $$
Intuitively, the less ano is, the more segregated the network is.
Date: 2025-01-13
Performer: Hiro KATAOKA (University of Tsukuba)
The whole experiment, from scratch, can be executed through:
Hardware: AMD EPYC 7302P, Memory 128GB
OS: Ubuntu 22.04.5 LTS x86_64
Nim version: 2.2.0
Simulator version: 747763f895c41497a0f7066177762ba2d7cb93ff
Before showing plots, we read all of the data and calculate the measures.
First, we try to reproduce the Figure 4a in Sasahara et al., 2020.
We could reproduce the figure.
Next, we try to reproduce the Figure 4b in the same paper.
We could reproduce the figure with different measures.
Sasahara et al. show the effect of how to rewrite the network when $\varepsilon=0.4$ where agents form two communities on average. What is the same condition here? In other words, which $\varepsilon$ should be used to produce two communities on average at the end?
From the table above, $\varepsilon=0.2$ yields $2.3$ groups on average. This is the same result as what Sasahara et al. shows because the opinion spaces are different each other.
To observe the network segregation, we plot similar one with ano.
It shows the complex lines:
Suppressing the experiments where agents form the single group can help us to observe these effects:
Small $\varepsilon$ and large $\varepsilon$ prevent agents from segregating.
In the following plots, agents are represented as circles. Each colors represent the opinions:
#0000FF);#FF0000);In other words, the lower (resp. higher) opinions are, the closer the colors represent them are to blue (resp. red).
We can observe similar effect for the value $V_b$.
The distribution of the final opinions from the same experiment 1439-0.15-15596-a as above:
We can reproduce the Figure 4 in Sasahara et al.; we can observe echo chambers.
This file can be retrieved from URL https://sake.re/20241209-BROD