This link takes you back to the CMOL page. To run the applet you will need to have a Java Runtime Environment installed.
The paper "Dynamics of Opinions and Social Structures" associated to the applet can be downloaded from arXiv:0708.0368.
74 agents connected by 100 links communicate with each
other to acquire new information about agents they find
What they find interesting is what their
friends talk about.
The node color between red and blue represents the relative
interest in the nodes marked with red and blue halos
and the Voting pattern shows this interest averaged over
The node size represents other agents interest in the
To get better access to new information,
an agent can use the friend who provided her with the most recent
information about the agent in question and establish a
new connection: the friend has a friend that has even
newer information about the interesting agent.
The communication level and the interest
allocation in the applet change the strength of the
three components of the model (for details see here):
Agents are not only interested in other agents, but
also that other agents are interested in them: when
interested agents work to get new information about a
specific agent they will at the same time provide their
information about other agents. Here we model
two different strategies to engineer other agents' interests:
- Communication with connected friends
- New friend via old friend
- Interest in specific information
An agent with the media strategy broadcasts information
about itself. For each communication event anywhere in
the system, randomly chosen agents convert together a
fraction of the total interest memory in the system to
M. Contrary, the politician uses its local
network to persuade other agents and imposes its
personality on the agent it talks to by converting a
fraction of this agent's interest memory to P.
- The media strategy M
- The politician strategy P
The strategy can be changed for two of the agents in the
applet. The leftmost agent's strategy can be changed by
the blue button "B(lue) strategy" and the rightmost agent's
strategy can be changed by the red button "R(ed) strategy".
The race between the two agents can be followed in the
voting pattern graph. The two lines indicate the agents'
proportionate interest in the red and the blue strategy
relative to the total interest in red and blue. The two filled
curves indicate the agent's total interest in the
two strategic agents relative to the total interest in all
agents (when no strategy is chosen, the voting pattern
represent the interest in the agents with the halos).
To garner votes the strategic agents can also be
antagonistic to their opponent. In this way they do not
win random interest from the agents they communicate with, but
interest that previosly was devoted to the opponent. This
strategy has the prefix a- (weak) or A- (strong) and is very powerful.
The weak antagonist can only directly win interest from
the opponent, whereas discussions about the strong
opponent by anyone always in first case eliminates interest about the opponent.
The networks can be laid out based on the degree of the
agents, the attention they have among other agents, or
how new information they have about other agents. Together they
represent the three main components of the model in the
To study a generated network in more detail, stop the simulation
and move around the agents with the left mouse
button by clicking and dragging. By repeatedly just
clicking without dragging the nodes relax by spring
forces. With the right button new links can be created or
removed between agents. Pushing down the middle mouse
button on a node and releasing it on another shows the
communication pathway from the first to the second
The reset button "Mem" clears all agents memory but preserves
the network structure and the reset button "Net" does the opposite.
This is a more technical description of the model not far
from a pseudocode.
The ratio between communication and rewiring in the system
is an important parameter set by the "Communication level"
in the applet.
- Memory Three vectors represent the memory of
an agent. Two of the vectors have the same length as
the number of agents N in the system to form a simple local map
of information flow in the network: one vector contains
the age of the information and the other the name of the
friend that provided it as a pointer toward the
information source. In this way every agent has an
idea about in which direction any other agent is in the
network as well as a proxy for its quality.
The third vector can have any length, set by the scroll
bar "Interest allocation" in the applet. This vector
represents an agent's interest in other
agents, by containing different proportions of the
agents' names. We have chosen to reserve an agent's first N-1
elements, one to each of the other agents in the system.
In this way every agents always have a finite
interest in everyone. The remaining elements in the
vector can be changed dynamically when the agents
communicate with each other. Local interest
allocation corresponds to a long vector with almost
complete dominance of personal interest over the fixed
"global" interest in everyone.
- Communication By choosing a link randomly
(communication constrained by links) or inversely
proportional to the degree in its ends (communication
constrained by nodes), we select two agents to talk
to each other. One of the agents choose the subject of the
randomly selecting an element, i.e. an agent proportional
to its occurrence in the interest
vector. The agents then compare how new information they
have about the agent of interest and the one with the
oldest information updates its memory: the age of the
information is copied from the agent with the
newest information and the pointer is updated to the
friend that just provided the information.
The two agents also update their interest vectors by
randomly assigning a position to each other as well as the
agent they talked about. This is where the politician strategy
differs from normal agents and an agent communicating with
a politician will
assign more than one position to the politician (the
number being set by the strength of the politician in the
An antagonistic politician, blue for example, attacks the
other agent's interest in blue's opponent red when the
assignment no longer is random, but purposefully to positions
previously assigned to red.
- Rewiring The agents strive to get better access to
information about other agents they are interested in. One
way is to communicate a lot, the other to shortcut the
communication pathways. In a rewiring step, a randomly
chosen agent chooses a random element in its interest
vector, and thereby the corresponding agent proportional
to how many time it occurs in the memory. The agent then
goes to the friend that provided the most recent
information about it to get information about where she in turn got
the information from. By establishing a link to its friend's
friend the agent has, if the information was correct and
updated, made the information pathway one step shorter. To
keep the number of links in the system balanced, we at the
same time remove a link randomly.
We increment the age of all information the agents' have
about each other after every L'th communication
event, L being the number of links in the
system. Because every agent has information with age 0 about
itself, the age of the information about any agent gets
older the further away it is from the agent in the
network. However, if an agent is very popular, the
information can travel far without getting much older.
In society one observes social groups with widely different music
tastes, religious beliefs, and languages. They emerge
and disappear on all scales from extreme subcultures to mainstream
massculture. Several positive feedback mechanisms drive the
diversity of beliefs in social systems. Some of these mechanisms can
be analyzed in terms of a hugely simplified model of a dynamic
network that incorporates basic feedback between information
assembly through communication and formation of social connections.
Our model consists of a social network of agents, each having a
memory. This individual memory is a simple local picture
of where other agents are in the network together with a
priority of relative interest in each agent.
The agents communicate with other agents and modify their
memory when they get new information about other agents.
Based on this memory they also build new social
connections to get better access to agents they find
The strong coupling between the agents' believes, the inner world, and their
positions in the dynamic network structure, the outer world, has interesting
consequences. The system can for example not be reset by either
resetting the agents' inner or outer world.
They have to be reset simultaneously, because
otherwise information about the old system will be stored in
the world that was not reset and enable a partial recovery
of the system.
In the model, a social system on the size of a large school class is
simplified into a number of agents. These agents form a network that
dynamically adjusts itself to facilitate a hunt-gatherer behavior
in information space, which in turn is reflected in a tribal
organization of the evolving social network. This tribal
organization is sensitive to information manipulation, as
illustrated by influence of particularly convincing demagogues.
The model allows us to consider the impact of certain charismatic
people. Thanks to their larger charisma, they can influence their
fellow agents to think disproportionately more about them, or
equivalently, about political objectives of which they
are the main representative. Thus, our model allows for new
analysis of the effects of celebrities,
politicians or prophets in a social system.
Scenario 1: Consider the introduction of a single politician, or of
several politicians or media persons. We find that the
associated engineering of communication tends to streamline the
social network into hierarchical structures around a celebrity
center of fashionable persons.
Scenario 2: If two politicians garner votes with different
strategies, one only by advocating for himself and the
other with an antagonistic strategy to purposefully win
votes from the other side, the antagonistic politician
will do much better.
The antagonistic strategy is so effective that it outcompetes a
much stronger win-any-vote-strategy.
Scenario 3: Two competing antagonistic politicians, form a
system where equal sharing of influence is unstable, in the sense that the system
tends to choose one of the candidates on the cost of the other. In
terms of biology or physics, the system develops bistability where a
monoculture dominates for long periods of time. The state of a
bistable system is historically dependent, determined on the few
times in history where the two conflicting beliefs are of equal
strength. It is tempting to compare persistent segregation in our
model with the geographical segregation of religious beliefs in the
Scenario 4: Consider half the population being liberal and
half the population being conseravtive — who will people think
about? To find out, press the button under Opinion
stubbornness and half the population will update their
interest 10 times faster (blue) than the other half (red).
Martin Rosvall and Kim Sneppen. "Opinion Formation and
Social Structure", arXiv:0708.0368.