Games and Crowds: Now, Near, Next
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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.
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Games and Crowds: Now, Near, Next
- 1. Games+Crowds Now. Near. Next. Ben Sawyer Digitalmill, Inc. bsawyer@dmill.com @bensawyer
- 2. About Me
- 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
- 9. Citizen Science NIH NSF Game Developers Everyday people Crowdsourced Clinical Trials Gathering data for science ! Analyzing data for science Multiplayer science games Birdcount
- 11. Wisdom or Intelligence? Wisdom of Crowds Collective Intelligence Collective Wisdom of Crowd Intellgience
- 12. “Crowdsourcing” Human Computation HC with games Citizen Science “Good” HC Games Drilling down… Internet Masses
- 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
- 14. Are games at the heart of crowdsourcing?
- 15. Engagement Behavior Learning Performance Motivation & Interface
- 18. YES!
- 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
- 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
- 29. Out & About Photos/Video Biometrics Physical tasks Mapping/Check-ins Out & About Coordinated Event Markup Language Groundcrew / Joe Edelmen (http://nxhx.org)
- 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
- 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
- 42. Games+Crowds Now. Near. Next. Ben Sawyer Digitalmill, Inc. bsawyer@dmill.com @bensawyer