DEPARTMENT OF SOCIOLOGY

Social Networks and the Book

Trade: An Outsider’s View

Edmund Chattoe-Brown

ecb18@le.ac.uk

http://www.le.ac.uk/sociology/staff/ecb18.html

Plan of the talk

• Something about simulation.

• Something about history.

• Something about networks.

• Something about the evolution of book markets.

Simulation?

• A very simple example: Not realistic and not historical, but the point will quickly become clear.

• Q: How do we explain urban residential segregation between ethnic groups?

The Schelling model

• Agents live on a square grid so each site has eight neighbour sites.

• There are two “types” of agents (red and green) and some sites in the grid are unoccupied. Initially agents and empty sites are distributed randomly.

• Each agent decides what to do in the same very simple way.

• Each agent has a preferred proportion (PP) of neighbours of its own kind (0.5 PP means that you want at least half your neighbours to be your own kind. Fractions are used so empty sites “don’t count” for satisfaction.)

• If an agent is in a position that satisfies its PP then it does nothing.

• If it is in a position that does not satisfy its PP then it moves to an unoccupied position chosen at random.

• Each time period is defined to allow each agent (chosen in random order) to “take a turn” at deciding and maybe moving.

Initial state

Two questions• What is the smallest PP (between 0 and 1) that will produce clusters?

• What happens when the PP is 1?

Two (surprising?) answers

• PP about 0.3. People don’t have to be “racist” to generate residential clusters. If you had seen the clusters in real data would you have “assumed” racism?

• As people get more “racist”, clustering gets “stronger” (clusters get more separate and have less contact being “buffered” by empty sites) but at some point, the clusters break down and with PP=1, the system looks no different from the random starting position.

Explaining complicated systems

• In the first case, because the PP is a minimum, anyone surrounded by their own kind is happy. This means being at the centre of a cluster is stable. Being at an edge is also stable as long as the edge isn’t too sharp (a curve but not a corner). Anyone not in a cluster or at an edge is always at risk of becoming unhappy by movement of others (nobody can move into a tight cluster) so no other arrangement is stable.

• In the second case, everybody wants to be in the middle and nobody is happy being an edge so clusters cannot form.

Simple individuals but complex system

Individual Desires and Collective Outcomes

-20

0

20

40

60

80

100

120

0 50 100 150

% Similar Wanted (Individual)

% Similar Achieved (Social)

% similar% unhappy

Can we have some history please?

• Clearly, the system and its agents have a “history” (sizes and shapes of clusters and moves from place to place by agents) but is this what we mean by History?

• Disciplines and methods are divided by hypotheses it is actually very hard to test (“articles of faith”).

• Agents all make decisions the same way and this (and the choice set) don’t change. [A FAIR COP!]

• Does the assumed particularity of history require individual and structural complexity? [NOT HERE.]

What about data?

• Statistical comparison of simulated clusters with real clusters from cities.

• Qualitative/ethnographic studies of relocation decisions in context. [Other methods: Experiments?]

• What about historical data? We know how the world was at particular times (co-publication) and we sometimes have insight into how people thought but the data is often scarce and if it is missing, it is missing.

• Do we trust sociological/behavioural insight over historical time? (RCT, FOAF.)

The aspiration

• Simulation helps us think clearly about dynamic social processes (history is presumably an example).

• It allows us to reflect explicitly on what we assume stays constant and what changes over time. (An assumption dividing methods: Behaviours versus “attributes”.)

• It allows us, in principle, to start with behavioural and contextual hypotheses and “run these forward” to see if emerging simulated structures parallel what is observed in the real world.

What has this to do with networks?

• Social Network Analysis can very easily and effectively characterise networks once they are got BUT:

• They may be hard to get. (Not always: Letter networks?)

• SNA is _not_ very good at linking dynamic network formation processes (the meat of history) to observed data. To caricature brutally, it either attributes model characteristics to individuals (random tie making) or starts with excessively simplified behavioural assumptions to look at “toy” dynamics.

A quick explicit networks example

• People meet on the street from time to time.

• The more often they meet the more likely they are to recognise/acknowledge each other.

• After a while, failure to acknowledge will itself reduce likelihood of acknowledgement (“cutting”/“looking through”).

• Acknowledgement leads to other kind of relation (conversation, discovery of shared interests …)

• This can be simulated. What do the resulting networks look like?

An example: Book market structure

• The set of customers and book sellers defines a potential network. (Leaving aside publishers, printers, distributors and other complexities for now.)

• What shape does this network have? How does the distribution of customers (and their demands) affect the structure of the market?

• How does this network evolve over time? Why (and when) do new book sellers come into existence?

• Why might there be a transitional phase of (hard to access) book peddling?

Some hypotheses for a simulation I• Book sellers and book buyers can stay still or move: Moving

sellers are peddlers.

• Book buyers have a regular small but slightly fluctuating disposable income for books.

• Demand for books is created both by reading them and by social influence from other buyers.

• Book sellers “fall from the sky” regularly but must cover recurring fixed costs to survive (buying for 0.5 and selling for 1 and only buying when they get short of stock and can afford it.)

• Buyers buy whatever they can afford at their current “demand level” every time they encounter a seller.

Some issues clearly not tackled

• Any set of simulation hypotheses draws attention to potential improvements and helps understanding.

• How do peddlers buy “in transit?”

• Buyers live forever: What happens to libraries? How important are second hand markets?

• Books are undifferentiated: Book shops can carry much larger stock than peddlers.

• No buyer loyalty (so networks evolve randomly or at best spatially).

Example I: Demand and wealth

The system has to adjust itself dynamically so that customers can spend all their disposable income on books given the overall demand and survival requirements of sellers.

Example II: Seller survival

If buyers won’t move then sellers have to and vice versa. Peddlers are red and fixed sellers are black.

More surprising results

Intuitively, using static market ideas, a large increase in entrants will increase competition and “clear out” the market (most will immediately go bust again). In fact, the market can sustain much higher numbers of sellers, they just have lower throughput. However, patterns of buyer adjustment and proportions of peddlers and shops stay similar.

More subtle weaknesses

• Books are distinctive. Ultimately demand is limited by publication rate and stock and markets saturate especially with s/h books existing. Do many houses only ever want a Bible and an Almanac?

• Both buyers and peddlers should have “routes”. This isn’t difficult technically but would have taken more time than I had. How important is spatial structure: Centres of population as “nodes” for bookselling. (Simplest quantitative model of all that a place reaching a certain size “predicts” a book seller: Old economic geography.)

Simulation as heuristic for research

• Which successful book sellers (or peddler routes) are counterfactuals to the hypotheses? University towns, areas of thinly scattered and stagnant population? Areas of non-conformism with personal faith based on self-study?

• What other forms of book selling existed? Did book sellers use peddlers as a delivery service? Did non specialist book sellers tend to pre-date or post-date specialists? Did large centres act as wholesalers for smaller centres? Where this happened, did it affect the feasibility of new growth?

Drawing it all together• Despite obvious limitations of the simulations presented (arising

from time constraints and the practicalities of presentation not technical restrictions of the method), simulation allows:

• Organised theorising on historical (or at least long term dynamic) processes. (Burke: Is the historians opposition to theory a reaction to grand theorising? Can history really do without some theories of social action? Better explicit than implicit?)

• “Formally” linking hypotheses about individual behaviour with observed macroscopic structures and properties.

• Explaining complex social systems that often behave counter-intuitively and suggesting new research questions/data collection objectives.

Now read on?• Journal of Artificial Societies and Social Simulation (JASSS):

• <http://jasss.soc.surrey.ac.uk/JASSS.html>

• simsoc (email discussion group for the social simulation community):

• <https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=SIMSOC>

• Simulation for the Social Scientist, second edition, 2005, Gilbert and Troitzsch.

• Simulation Innovation, A Node (Part of ESRC National Centre for Research Methods, conducting research, training and outreach in social simulation):

• http://www.simian.ac.uk, http://www.ncrm.ac.uk

• NetLogo (software used for these examples, free, works on Mac/PC/Unix and comes with standard library of example programmes):

• <http://ccl.northwestern.edu/netlogo/>

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• I’d like to take these ideas on in collaboration with a historian, with a view to funded research/a PhD award.