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Summary Network economics
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Network Economics - Radboud University
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Network economics week 1 readingsSome questions you may wish to address:
- Why was this article written?
- Who is the intended audience?
- What have I learned from reading this article?
- What questions am I left with after reading this article?
- What is missing/could have been included in this article?
Networks, crowds and markets; chapter 1
Network = a pattern of interconnections among a set of things
Understanding any one piece of information in a network environment depends on understanding
the way it is endorsed by and refers to other pieces of information within a large network of links.
In the most basic sense, a network is any collection of objects in which some pairs of these objects
are connected by links.
When people talk about the ‘connectedness’ of a complex system, they are talking about two related
issues. One is connectedness at the level of structure – who is linked to whom – and the other is
connectedness at the level of behaviour – the fact that each individual’s actions have implicit
consequences for the outcomes of everyone in the system.
A fundamental point is that, in a network setting, you should evaluate your actions not in isolation
but with the expectation that the world will react to what you do.
From economics we contribute to network economics by drawing on models for the strategic
behaviour of individuals who interact with each other and operate as members of larger aggregates.
Two main bodies of theories in network economics are graph theory and game theory.
Graph theory is the study of network structure, while game theory provides models of individual
behaviour in settings where outcomes depend on the behaviour of others.
Strong ties represent close and frequent social contacts in social network analysis, whereas weak ties
represent more casual and distinct social contacts.
Structural holes are parts of the network that interact very little with each other.
Structural balance.
The discussion of game theory starts from the observation that there are numerous settings in which
a group of people must simultaneously choose how to act, knowing that the outcome will depend on
the decisions made by all of them.
Braess’s Paradox.
Equilibrium is a state that is ‘self-reinforcing’ in that it provides no individual with an incentive to
unilaterally change his or her strategy, even if that individual knows how others will behave.
The interactions among buyers and sellers, or pairs of counterparties to a trade or loan, naturally
forms a network.
Social practices.
At a surface level, one could hypothesize that people imitate the decisions of others simply because
of an underlying human tendency to conform: we have a fundamental inclination to behave as we
see others behaving.
Information cascades happen where even rational individuals can choose to abandon their private
information and follow a crowd.
There is another important class of reasons why people may imitate the behaviour of others. When a
direct benefit can be gained from aligning one’s behaviour with that of others, regardless of whether
they are making the best decision.
Network effects amplify the success of products and technologies that are already doing well; in a
market where network effects are at work, the leader can be difficult to displace. A social network
becomes more and more valuable as the number of users increases.
, The diffusion of technologies can be blocked by the boundary of a densely connected cluster in the
network – a ‘closed community’ of individuals who have a high amount of linkage among themselves,
and hence are resistant to outside influences.
Search is the way people can explore chains of social contacts for information or referrals to others.
Institutions can be any set of rules, conventions, or mechanisms that serve to synthesize individual
actions into a pattern of aggregate behaviour.
Prediction markets use a market mechanism to provide predictions of future events such as the
outcomes of elections.
Voting is a social institution that aggregates behaviour across a population.
Networks, Crowds and Markets; Chapter 2, Graphs
A graph is a way of specifying relationships among a collection of items.
A graph consists of a set of objects, called nodes, with certain pairs of these objects connected by
links called edges.
Two nodes are neighbours if they are connected by an edge.
Asymmetric relationships: that A points to B but not vice versa.
A directed graph is a set of nodes together with a set of directed edges; each directed edge is a link
from one node to another, and the direction is important.
An undirected graph is undirected. In general, the graphs are undirected unless noted otherwise.
A communication network, in which nodes are computers or other devices that can relay messages
and the edges represent direct links along which messages can be transmitted.
Two broad classes of graph structures: social networks, in which nodes are people or groups of
people and edges represent some kind of social interaction, and information networks, in which the
nodes are information resources and edges represent logical connections.
A path is a sequence of nodes with the property that each consecutive pair in the sequence is
connected by an edge.
In transportation networks, nodes are destinations and edges represent direct connections.
A dependency network, in which nodes are tasks and directed edges indicate that one task must be
performed before another.
A structural network, which has joints as nodes and physical linkages as edges.
The area of rigidity theory, which is at the intersection of geometry and mechanical engineering,
studies the stability of such structures from a graph-based perspective.
A simple path is a path that does not repeat nodes.
A cycle is a nonsimple path, which informally is a ring structure. A cycle is a path with at least three
edges, in which the first and last nodes are the same, but otherwise all nodes are distinct.
A graph is connected if, for every pair of nodes, there is a path between them.
If a graph is not connected, then it breaks apart naturally into a set of connected ‘pieces’ – groups of
nodes with the property that each group is connected when considered as a graph in isolation, and
no two groups overlap.
A connected component (= component) of a graph is a subset of the nodes such that (i) every node in
the subset has a path to every other and (ii) the subset is not part of some larger set with the
property that every node can reach every other.
A giant component, which is a deliberately informal term for a connected component that contains a
significant fraction of all nodes. When a network contains a giant component, it almost always
contains only one.
The length of a path is the number of steps it contains from beginning to end – the number of edges
in the sequence that comprises it.
The distance between two nodes in a graph is defined as the length of the shortest path between
them.
Breadth-first search is a technique that searches the graph outward from a starting node, reaching
the closest nodes first.
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