What determines an individual's power?
Is it an individual characteristic?
A network property?
"Indeed, as Richard Emerson has observed in his fundamental work on this subject, power is not so much a property of an individual as it is a property of a relation between two individuals -- it makes more sense to study the conditions under which one person has power over another, rather than simply asserting that a particular person is "powerful". E & K, 340
In :%matplotlib inline import networkx as nx import matplotlib.pyplot as plt g = nx.Graph([("A", "B")]) nx.draw_networkx(g)
If we assume that a relationsip holds some sort of value, how is that value divided?
Think about it...what kinds of value could a relationship hold?
If we think about power in terms of an imbalance in social exchange, how is the value of a relationship distributed based on the power of the individuals of the network?
Where does power come from?
Network Exchange Theory addresses questions of social imbalance and its relation to network structure.
In :g.add_edges_from([("B", "C"), ("B", "D"), ("D", "E")]) nx.draw_networkx(g)
Dependence - if relationships confer value, nodes A and C are completely dependent on node B for value.
Exclusion - node B can easily exclude node A or C from the value conferred by the network.
Satiation - at a certain point, nodes like B begin to see diminishing returns and only maintains relations from which they can receive an unequal share of the value.
Betweenness - can confer power, this sort of centrality allows nodes like B to take advantages of structural holes and also control the flow of information througout the network. Note: high betweenness does not always confer an advantage in bargaining situations (as we will soon see).
Recall the experimental methodology typically used to study power and exchange? Get together with your pods and refresh your memories...there are five steps.
Are the results of these experiments considered to be robust?
Why or why not (according to E & K)?
In :g = nx.Graph([("A", "B")]) nx.draw_networkx(g) plt.title("2-Node Path")
Out:<matplotlib.text.Text at 0x7fd67ca1a690>
In :g = nx.Graph([("A", "B"), ("B", "C")]) nx.draw_networkx(g) plt.title("3-Node Path")
Out:<matplotlib.text.Text at 0x7fd67c957690>
In :g = nx.Graph([("A", "B"), ("B", "C"), ("C", "D")]) nx.draw_networkx(g) plt.title("4-Node Path")
Out:<matplotlib.text.Text at 0x7fd67c8aea90>
In :g = nx.Graph([("A", "B"), ("B", "C"), ("C", "D"), ("D", "E")]) nx.draw_networkx(g) plt.title("5-Node Path")
Out:<matplotlib.text.Text at 0x7fd67c7ee350>
In :g = nx.Graph([("A", "B"), ("B", "C"), ("B", "D"), ("C", "D")]) nx.draw_networkx(g) plt.title("Triangle with outlier")
Out:<matplotlib.text.Text at 0x7fd67c6cdb50>
In :g = nx.Graph([("A", "B"), ("B", "C"), ("C", "A")]) nx.draw_networkx(g) plt.title("Triangle")
Out:<matplotlib.text.Text at 0x7fd67c621810>
Don't worry! We won't start hallucinating Russian conspiracies!
In order to draw distinction between equal and asymetrical power distribution over an edge; between strong power and weak power, and between networks that stabilize and networks that don't, we can use a solution proposed by John Nash, the Nash bargaining solution.
The negotiation (in terms of competition) becomes about how to split the surpluss:
If A and B have equal power, we expect that each individual receives half of the surplus, so the outcome is equidependent:
How did perception of status effect the outcomes of empirical studies of the Nash bargaining soluiton?
(i) Person A is givin a dollar and told to propose a division of it to person B. That is, A should propose how much he keeps for herself, and how much she gives to B.
(ii) Person B is then given the option of approving or regecting the proposed division.
(iii) If B approves, each person keeps the proposed amount. If B rejects, then each person gets nothing.
What are the experimental results of the ultimatum game? Would you walk away with only 1% if you were person B?
How do we predict the outcomes of network exchanges on an arbitrary graph?
Well...what is an outcome?
A matching on a the network that specifies who exchanges with whom. A node may at most complete one exchanges, others may be left out and complete none.
A number that is associated with each node that indicates how much value the node receives from its exchange. If two nodes are matched in the outcome, the sum of their values must equal 1. An unmatched node has the value of 0.
Based on the above definition of stability, why are (a) and (c) not stable outcomes?
An instability can be defined as follows:
Therefore, an outcome of network exchange is stable if and only if it contains no instablities.
This sort of prediction is limited in that humans do not usually acheive the kinds of extremes presented by this model, furthermore, it creates ambiguity in more complex situations as in (c) and (d) above.
To account for these limitations, we can consider outcomes from the perspective of the Nash bargaining solution. Here we will consider related nodes in the network as the providers of outside options.
We can define a balanced outcome as follows: