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rationale. existing system VS culture as a function propagated by the masses generate predictive data increase public awareness for the arts increase responsibility of grant makers. Sub-domain examples. Cinema Museum exhibition City Art Project/Installation. film festival.

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Webdia mit

rationale

existing system VS

culture as a function propagated by the masses

generate predictive data

increase public awareness for the arts

increase responsibility of grant makers


Sub domain examples
Sub-domain examples

  • Cinema

  • Museum exhibition

  • City Art Project/Installation


Film festival
film festival

  • Predicting the top box office and the cumulative gross over some period

  • Categorized profile of each person

    • in the dimensions such as genre preference, age, sex, etc.

  • Type of each movie

    • in the dimensions such as genre, nationality, big stars shown, etc.

  • For classifying,

    • survey

      • preferences on different genres

      • identifying genre for each movie

      • predictions on the numbers for already known results of films that had similar success

    • data collection

      • the movies they watched in the festival

  • Observing how people’s different profiles influence predictions on different types of movies

    • adjusting the sensitivity and bias of each person’s prediction for more accurate prediction result

  • Depending on the true result, participants get the rewards


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prediction system

(sensitivity and bias

parameters, etc)

information

each person’s

prediction & betting

prediction result

parameter tuning

influence

initialization

deviation from the true result

profile classification

(e.g. survey, data collection)

each person’s

profile,

the type of each

item

inferred

  • - The parameters in the prediction system can be learned by

  • adjusting the difference between the prediction result and the true result.

  • Deciding the number of dimensions describing the personal profile

  • and the item type (Model Selection by Occam’s razor)


Exhibitions in museums

Exhibitions in Museums

Predicting the success of exhibitions held in Museums


Predicting the success of exhibitions held in museums

Museum Administrator and Project Managers

Allocating exhibition space and time.

Decision making on extending or closing the exhibition

Participants

Reward by predicting the truth result

Opportunity to contribute public arts

Predicting the success of exhibitions held in Museums



Paris collections 2006 at boston mfa
Paris Collections 2006 at Boston MFA

Dec 16, 2006 - Jun 17, 2007 Michael Mazur: The Art of the Paint

Jan 10, 2007 - Oct 8, 2007

Women of Renown: Female Heroes and Villains in the Prints of Utagawa Kuniyoshi (1797-1861)

Jan 24, 2007 - Jul 8, 2007

Donatello to Giambologna: Italian Renaissance Sculpture at the Museum of Fine Arts, Boston

Mar 10, 2007 - Aug 1, 2007

Through Six Generations: The Weng Collection of Chinese Painting and Calligraphy


Body world 2 at boston science museum
Body World 2 at Boston Science Museum


Body world 2 at boston science museum1
Body World 2 at Boston Science Museum

  • » Fish, Fads, and Fireflies

  • » Is Algae In Your Future?

  • » Dinosaurs

  • » Lighthouse

  • » Human Body Connection

  • » Investigate!

  • » Natural Mysteries

  • » Science in the Park

  • » Theater of Electricity

  • » Discovery Center

  • » Current Science & Technology

  • » Cahners ComputerPlace

  • » Gilliland Observatory

  • » Welcome to the Universe



Exhibitions in museums1
Exhibitions in Museums

  • Predicting the number of cumulative visitors within a month

  • Participants’ preferences

    • survey

    • data collection through records and sensors.

  • Correction of error by observing how participants’ preferences influence predictions on different types of exhibitions

  • Rewards to participants based on the true result.


Art projects
Art Projects

  • Predicting the budget allocation and success.

  • Limiting the tickets playing prediction for each project.

  • Make a prediction of success per ticket.

  • Rewards to participants based on the true result.


Webdia mit

how

Funding

levels

Buy-in

Cost

Different Projects

Time

Multi-stage betting system

Predict success of project

Periodically publish results

Adjust buy-in to reflect availability of information

Project installation closes house at a given cell

Success evaluated by independent agency


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success

Funding

levels

In multiple domains:

property value

attendance (technological implications)

Different Projects


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cant go dynamic

Funding

levels

Buy-in

Cost

Share price determined

dynamically

Transaction wipes cards

clean

Different Projects

Time

Punishment for predicting early is too great

An individual’s prediction matrix is too complex to convey in an ask-bid

Wiping inordinately punishes for buying early


Webdia mit

  • Goal: Predict impact of funding

    • Many possible projects

    • Many possible levels of funding

  • Problem: Not all projects get funded -- no feedback on predictions for unfunded projects


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  • Reward based on some projects only:

    • Collect predictions (of attendance, approval) for all projects

    • Assign reward based on available data only (only projects that are ultimately funded)

  • Alternative: reward based on outcomes measurable irrespective of funding choice (neighborhood income, property value)

    • Are people already good at predicting local economic trends likely to be better at predicting impact of arts projects on those trends?


Possible strategy
Possible strategy

  • I want project B to get funded, pick accurate estimate of project B’s benefit, slightly lower estimate for all others.


Model
Model

  • For model, remove funding level variable:

    • “If given reasonable funding, how much relative benefit from each?”