This presentation covers ideas and issues related to the use of games and videogame technologies in crowdsourcing projects for productivity, education, citizen science, human computation, and more.
8. Language & Models
Games with a
Purpose
Citizen Science
Wisdom of
Crowds
Collective
Intelligence
Human
Computation
Crowdsourcing Crowdfunding
Game-based
Crowdsourcing
Distributed
Thinking
Participatory
Sensing
Multiplayer
Games
Social Games
13. Language?
• Games with a purpose
• game-based [crowdsourcing | human computation]
• Citizen science needs one definition across agencies
and fields
• gamification != games
• wisdom of crowds != collective intelligence
• crowdsourcing != crowdfunding
• Game-based crowdsourcing FOR citizen science
20. Gamification?
• “Game Layer” vs. Game
• Additive to game vs. additive to exercise
• Silo’d gamification
• Process of play vs. task rewarding
• Intrinsic vs. extrinsic motivation
21. Work to Date…
50+ projects, health heavy, university majority,
many CS experiments
22.
23. AI Collection
Puzzle/Challenge/Work!
Wisdom of Crowds!
(shared work)!
Puzzle/Challenge/Work!
Collective Intelligence!
(individual efforts)
Ideation, and Data
Collection
Summary
Players performance is
used to source new
forms of AI
Users share work and
build upon the
collective effort of
others
Individuals solve
puzzles on their own
and little is shared
between players
Players produce new
data from-scratch often
ideas, new forms of
rhetoric or
geographically located
data
Example Restaurant Game Tag Challenge Fraxinus World Without Oil
Uniqueness
Player is focused on
game objective and
computer observes
their performance vs.
other humans or
computer opponents
and learns from it.
Players work together
and share work
toward optimized
results they develop
by observing each
other
Players work
independently of each
other but the system
combines their
collective work into
higher-end results
Players produce
original work and
often judge each
others (or are subject
to independent human
judges) work in order
to play and arbitrate
the game
Core Uses
24. Core User Performance Summary Example
Creating Data
Game incentivizes players
to collect or generate new
data from sratch
Photocity
Transforming Data
Game presents data for
player to sort, match,
identify or otherwise
transform
Phylo
Augmenting Data
Player analyzes and
annotates it with additional
data and meta-data
Metadata Games
User Performance
25. CROWD MODEL VARIANT I VARIANT II VARIANT III Notes
Processing Human-in-Loop Human+Computer
Participatory for
Engagement
In human-in-loop processing, the human is necessary
to computation for specialized capabilities, for
machine donated resources crowdsourcing is used to
gain access to CPUs, power, and storage necessary
to crunch the large amount of data. Participatory for
engagement means that humans are helping process
data but not because the computer isn’t capable but
because there is a need to engage people in the
process for alternative outcomes.
Observable Gameplay
Semantics & Natural
Language Processing
Social Graph
Observable crowdsourcing means that the players
actions are observed and sourced as data toward a
higher-end outcome (e.g. AI opponents, language
parsers). Gameplay is useful especially for AI,
Semantics and Natural Language Processing gains
from human interactions (e.g. see Restaurant Game).
Finally observing social graphs can help gain
additional crowdsourced data.
Physical
Environmental/
Geolocation Data
Capture
Capture
Physiological Data
Capture Transactional
Data
Physical crowd models distinguish themselves by
capturing data that requires humans to produce or
capture. Environmental and geolocation data can
involve photos, flora/fauna samples, 1st person
observations, periodic mobile sensor readings, etc.
Physiological data is self-report, or sensor captured
biometrics and emotional health reports.
Transactional data while possibly captured through
computer networks still requires a real-world human
decision to initiate the underlying transaction.
Crowd Models
26. Type User Performance Crowd Model
Puzzle/Challenge/
Work
Processing Data Processing
Ideation / Data
Collection
Creating Data Physical
AI Collection Augmenting Data Observable
Crowd Models
27. Type Player Performance Crowd Model
Puzzle/Challenge/
Work
Transforming Data Processing
Ideation / Data
Collection
Creating Data Physical
AI Collection Augmenting Data Observable
Examples!
Puzzle/Challenge/Work→Augmenting Data→Processing
AI Collection→Creating Data→Physical
Ideation/Data Collection→Transforming Data→Observable
28. Roleplaying
Player takes on a specific role within a game world
which contributes to data capture & generation
Restaurant Game
Ideation
Ideas are generated and posted by players and
captured for later analysis
World Without Oil
Strategy &
Puzzles
Player performs in the game as designed which
enables capture of gameplay for generating better
AI for future players
Project Augur
Arcade &
Physical Play
Hand-eye coordination ???
Sensory Acuity
Often visual or audio based, player essentially is
using their sensory capabilities to perform a task
MalariaSpot
Assembly Player is “building” some structure ???
Game Genres & Activities
30. Talent Development
Attract people to a problem space
Cross-train their unique skills to other vertical
Motivate them to participate, and excel
Recruit
People Sourcing
31. Interesting…
• Humans as mobile sensors & cheaper robots
• Rhetorical systems, games that organize human
communication toward ideas, policies, and social
change (McGonigal)
• Lowering costs for production, and common problem
sets
• Identifying means to share communities
• Cross training as crowdsourcing need…
32. Human Joysticks
T U R N I N G R E A L - W O R L D H U M A N A C T I V I T Y &
D E C I S I O N S I N T O I N P U T S T H A T D R I V E G A M E S Y O U
P L A Y A S Y O U G O A B O U T Y O U R D A Y
Y O U R A C T I O N S A R E T H E J O Y S T I C K
35. Education & Skill
• How do we situate the player - what is their epistemic
frame? (Schaefer)
• Do we connect the actions in the game not just to the
story of science but the process as well?
• Do we purposely build games that can be done without
humans but use HC patterns to create new forms of
participatory science?
• Can we identify skills that might transfer to other
elements of life & economic activity?
36. Needs
• improving audience interaction & adherence
• discovery : beyond science geeks & especially
students
• better games and interfaces
37. Better games?
• Better games come from better projects with
experienced talent not from better templates!
• How can we bring together games industry with
science? This includes indies, top uni programs, jams,
etc.
• Are there engines and services we can define and
optimize?
• Process of play, interface, learning & immersion not
just productivity
38.
39.
40.
41. Recommendations
• Improve standard language
• Goal must be better game experiences!
• Identify common tools (especially game ones)
• Better include learning specialists
• Improve game-industry collaboration
• Identify means for problem holders to more easily
engage