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SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN www.si.umich.edu Where Do We Come From? What Are We? Where Are We Going? Thomas Finholt School of Information University of Michigan

Where Do We Come From? What Are We? Where Are We Going?

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Where Do We Come From? What Are We? Where Are We Going?. Thomas Finholt School of Information University of Michigan. Where Do We Come From? What Are We? Where Are We Going? , 1897, oil on canvas, Museum of Fine Arts, Boston. Data as the instrument. “by-products as products”. Examples. Past - PowerPoint PPT Presentation

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SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Where Do We Come From? What Are We? Where Are We Going?

Thomas FinholtSchool of InformationUniversity of Michigan

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Where Do We Come From? What Are We? Where Are We Going?, 1897, oil on canvas, Museum of Fine Arts, Boston

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Data as the instrument

“by-products as products”

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Examples

Past– public health reporting

Present– virtual observatory

Future?– car versus deer

Source: http://www.ph.ucla.edu/epi/snow.html

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Network as the instrument

“sensors, everywhere, joined”

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Examples

Past– Bell system

Present– GPS and TEC plots

Future?– computational and data grids

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Global GPS Network (November 1996): Coverage at Ionospheric Heights

10 degree elevation mask. Intersection height of 400 km.

Source: http://iono.jpl.nasa.gov/sitemap.html

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Source: http://iono.jpl.nasa.gov/latest_rti_global.html

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Simulation as the instrument

“seeing beyond the field-of-view”

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Examples

Past– physical models

Present– theory/data closure

Future?– multi-scale

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

UARC: Simulation and observational data

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Source: http://sparc-1.si.umich.edu/sparc/central/page/TomsTINGvsObserved

SPARC: Simulation and observational data

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Challenges

Source: American Automobile Manufacturers Association, http://www.automuseum.com/carhistory.html

Attempts to apply new technology are often framed in terms of familiar technology

First efforts are often awkward hybrids

It is hard to know where the seeds of greatness might lie...

Charles King’s “horseless carriage” (1896) Detroit, Michigan

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

The culture of simulation

Concrete

Exploratory

Improvisational

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Derive a simulation design aesthetic

What makes a design good?– Mutability

Who does the designing?– “just plain folks”

What is a signature design achievement?– the Sims

Source: http://www.ea.com/eagames/official/thesimsonline/home/index.jsp?

How to tinker

Source: http://www.tam.cornell.edu/~ruina/hplab/

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Tinkerers as change agents

They make sense of the world in light of experience

They need to play with applications to appreciate their function

True requirements may only become apparent after false starts

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Tinkering skills

Empathy -- can you see things through the user’s eyes?

Flexibility -- can you experiment?

Plagiarism -- can you find and assimilate successful innovations from other systems and services?

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Human-centered tinkering

Define requirements in terms of observed models

Test hypotheses in actual communities Use feedback to improve systems and

services

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Conceptualize:Observe models

Observe

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Build:Intervene

Conceptualize:Observe models

Observe, Build

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Build:Intervene

Trials: Deploy, use, evaluate

Conceptualize:Observe models

Observe, Build, Test

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Observe, Build, Test, Modify

Build:Intervene

Trials: Deploy, use, evaluate

Modify:extend design,evolution

Conceptualize:Observe models

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

UARC 5.0 interface

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

UARC 6.0 interface

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

SPARC interface

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

NEESgrid interface

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

NEESgrid interface

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

Wired VS reality

More

Time

Performance

Less

hype

raw performance of technology

“real performance”

“reality gap”

SCHOOL OF INFORMATION UNIVERSITY OF MICHIGANwww.si.umich.edu

What keeps designers honest?

Give users objects to think with (scenarios, mock-ups, prototypes)

Be patient…let users convince themselves

Know where you’ve been (collect baseline data) and what’s changed (collect data as you go along)