Abstract
The future of human computation (HC) benefits from examining tasks that agents already perform and designing environments to give those tasks computational significance. We call this natural human computation (NHC). We consider the possible future of NHC through the lens of Swarm!, an application under development for Google Glass. Swarm! motivates users to compute the solutions to a class of economic optimization problems by engaging the attention dynamics of crowds. We argue that anticipating and managing economies of attention provides one of the most tantalizing future applications for NHC.
Our sincere thanks to Pietro Michelucci for his prompt, helpful, and encouraging comments on drafts of this paper. His patience and assistance in the production of this paper has been invaluable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Of course, bees and flowers achieved this stable dynamic through millions of years of mutualistic interaction; as we discuss in “Developing the Attention Economy”, we expect any HC technique to require some period of adaptation and development.
- 2.
We ignore for the sake of the example any potential feedback from advertising or other systems that reinforce tweeting behavior surrounding the American Idol event.
- 3.
From the UK-based Six to Start. https://www.zombiesrungame.com/
- 4.
Glass is a wearable computer designed and manufactured by Google. The Glass headset features a camera, microphone with voice commands, optical display, and a touch-sensitive interface. It duplicates some limited functions of a modern smartphone, but with a hands-free design. Figure 1 depicts a user wearing a Google Glass unit.
- 5.
Complete game bible can be found at http://www.CorporationChaos.com.
- 6.
An attractor is just a location or state in a system toward which nearby states or locations tend to be “sucked.” Minimum-energy states in mechanical system are commonly attractors. For instance, in a system consisting of a marble confined to the inside of a mixing bowl, the state in which the marble is at rest at the bottom of the bowl is an attractor: no matter where you start the marble, it will eventually end up at rest at the bottom of the bowl. For an accessible introduction to the language of attractors and dynamical systems theory, see Strogatz (2001) and Morrison (2008).
- 7.
Credit goes to Robert Scoble for raising the example during a recent conversation about Swarm!.
- 8.
- 9.
The definition of “optimal” is disputed, but the discussion here does not turn on the adoption of a particular interpretation. In general, recall that solving the economic optimization problem involves deciding on a distribution of finite resources (labor, natural resources, &c.). Precisely which distribution counts as “optimal” will depend on the prioritization of values. A robust literature on dealing with conflicting (or even incommensurable) values exists. See, for example, Anderson (1995), Chap. 13 of Raz (1988), and Sen (1997).
- 10.
As opposed to value relative to exchange. See Marx (1859).
References
Ames M, Naaman M (2007) Why we tag: motivations for annotation in mobile and online media. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 971–980
Anderson E (1995) Value in ethics and economics. Harvard University Press, Cambridge
Anderson DP (2004) Boinc: a system for public-resource computing and storage. In: Grid computing, 2004. Proceedings of Fifth IEEE/ACM international workshop. IEEE, pp 4–10
Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15): 2787–2805
Ciulla F, Mocanu D, Baronchelli A, Gonçalves B, Perra N, Vespignani A (2012) Beating the news using social media: the case study of American Idol. EPJ Data Sci 1(8):1–11
Deterding S, Sicart M, Nacke L, O’Hara K, Dixon D (2011) Gamification. Using game-design elements in non-gaming contexts. In: PART 2—Proceedings of the 2011 annual conference extended abstracts on human factors in computing systems. ACM, pp 2425–2428
Dorigo M, Bonabeau E, Theraulaz G (2000) Ant algorithms and stigmergy. Future Gen Comput Syst 16(8):851–871
Fehr E, Schmidt K (2006) The economics of fairness, reciprocity and altruism—experimental evidence and new theories. In: Kolm S, Ythier J (eds) The handbook of the economics of giving, altruism and reciprocity, vol 1. Elsevier, London, pp 616–691
Gordon DM (2010) Ant encounters: interaction networks and colony behavior. Princeton University Press, Princeton
Greene K, Thomsen D, Michelucci P (2012) Massively collaborative problem solving: new security solutions and new security risks. Secur Inform 1(1):1–17
Hayek F (1948) Individualism and economic order. The University of Chicago Press, Chicago
Hodson H (2012) Google’s Ingress game is a gold mine for augmented reality. New Sci 216(2893):19
Khatib F, Cooper S, Tyka MD, Xu K, Makedon I, Popović Z, Baker D, Players F (2011) Algorithm discovery by protein folding game players. Proc Natl Acad Sci 108(47):18949–18953
Kocherlakota N (1998) Money is memory. J Econ Theory 81(2):232–251
Luca M (2011) Reviews, reputation, and revenue: the case of Yelp.com (no 12–016). Harvard Business School, Cambridge
Marx K (1859) A contribution to the critique of political economy, International Publishers, New York, 1979
McGonigal J (2011) Reality is broken: why games make us better and how they can change the world. Penguin Books, New York
Morrison F (2008) The art of modeling dynamic systems: forecasting for chaos, randomness and determinism. Dover Publications, New York
Norman A (1996) Computability, complexity, and economics. Comput Econ 7(1):1–21
Rashid AM, Ling K, Tassone RD, Resnick P, Kraut R, Riedl J (2006) Motivating participation by displaying the value of contribution. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 955–958
Raz J (1988) The morality of freedom. Oxford University Press
Sen A (1997) Maximization and the act of choice. Econometrica 65(4):745–779
Simon H (1971) Designing organizations for an information-rich world. In: Greenberger M (ed) Computers, communication, and the public interest. The Johns Hopkins Press, Baltimore, pp 37–52
Sterling B (2005) Shaping things. MIT Press, Cambridge
Strogatz S (2001) Nonlinear dynamics and Chaos: with applications to physics, biology, chemistry, and engineering . Westview Press
Taylor MA, Woolley JE, Zito R (2000) Integration of the global positioning system and geographical information systems for traffic congestion studies. Transport Res Part C Emerg Technol 8(1):257–285
Vargo SL, Maglio PP, Akaka MA (2008) On value and value co-creation: a service systems and service logic perspective. Eur Manage J 26(3):145–152
Velupillai K (2000) Computable economics. Oxford University Press, New York
Von Ahn L, Dabbish L (2008) Designing games with a purpose. Commun ACM 51(8):58–67
Von Ahn L, Maurer B, McMillen C, Abraham D, Blum M (2008) reCAPTCHA: human-based character recognition via web security measures. Science 321(5895):1465–1468
Weng L, Flammini A, Vespignani A, Menczer F (2012) Competition among memes in a world with limited attention. Sci Rep 2:335
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Estrada, D., Lawhead, J. (2013). Gaming the Attention Economy. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_75
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8806-4_75
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8805-7
Online ISBN: 978-1-4614-8806-4
eBook Packages: Computer ScienceComputer Science (R0)