Predictability and Prediction for a Media-Experimental Cultural Market: Difference between revisions

From Technologies and Politics of Control
Jump to navigation Jump to search
 
Line 32: Line 32:
'''More'''
'''More'''
* [[Data sources for measuring online activity]]
* [[Data sources for measuring online activity]]
* [[Schema for institutional ecology]]
* [[Locus of Control in Online Environments]]
* [[Locus of Control in Online Environments]]
* [[Adamic and Glance]]
* [[Adamic and Glance]]
* [[Predictability and Prediction for a Media-Experimental Cultural Market]]
* [[Predictability and Prediction for a Media-Experimental Cultural Market]]
* [[Network readiness index and web index]]
* [[Network readiness index and web index]]
* [[Communication platform design characteristics (public sphere)]]
* [[Criteria for the measurement of the impact of the internet in society]]
* [[Criteria for the measurement of the impact of the internet in society]]
* [[Shift on internet studies]]
* [[Shift on internet studies]]
* [[Direct measures of the internet (ISP/ICT)]]
* [[Direct measures of the internet (ISP/ICT)]]


|
|

Latest revision as of 16:17, 9 June 2014

(model idea)

Internet users are often influenced by the behavior of others, often because they want to acquire the benefits of coordinated actions or infer otherwise inacessible information. In these cases this influence decreases the ex ante predictability of the ENSUING social dynamics. These same social forces can increase the extent to which the outcome of a social process can be predicated very early in the process. A media market model that asseses the predictability of outcomes through formal analysis and the usage of insights deriving from this analysis can result into the development of algorithms for predicting market behavior at its early stage. The utility of the predictive algorithms can be illustrated through analysis of data sets of the market.


Processes in which observing a certain behavior increases the individual’s probability of adopting the behavior are often referred to as positive externality processes (PEP). Individual preferences and opinions are mapped to collective outcomes through an intricate, dynamical process in which people react individually to an environment consisting of others who are reacting likewise. Because of this feedback dynamics, collective outcomes can be quite different from those implied by simple aggregations of individual preferences. Standard prediction methods, which typically are based (implicitly and explicitly) on and an aggregation ideas, do not capture these dynamics and therefore are often unsuccessful. The feedback dynamics which reduces PEP predictability based on simple preference aggregation may increase the predicitive power of very early measurements of these dynamics. Again, the intuition is clear: early trends are reinforced through the positive feebacks of PEP, suggesting the possibility that early rankings of alternatives may be informative concerning the ultimate outcomes. We could now explore this intuition more formally.

Model will be prepared by Shoan Wu


References

Salganik, "Experimental study of inequality of unpredictability in an artifical culture market"

Bikhchandani,Hirschleifer,"Learning from the behavior of others"

Hedstrom, Sandell, Stern, "Mesolevel networks and the diffusion of social movements"

Shiller,"Irrational Exuberance"

Rogers, "Diffusion of Innovations"

Colbaugh, Glass, "Predictability analysis for Social Process"

Ormerod, Colbaugh, "Cascades of Failure and Extinction in evolving Complex Systems"

Theodor Kolovos

More

Participating @:

Useful Links:

My Info