When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents
We expect that human organizations and cooperative animal groups should be optimized for collective performance. This often involves the allocation of different individuals to different tasks. Social insect colonies are a prime example of cooperative animal groups that display sophisticated mechanisms of task allocation. Here we discuss which task allocation strategies may be adapted to which environmental and social conditions. Effective and robust task allocation is a hard problem, and in many biological and engineered complex systems is solved in a decentralized manner: human organizations may benefit from insights into what makes decentralized strategies of group organization effective. In addition, we often find considerable variation among individuals in how much work they appear to contribute, despite the fact that individual selfishness in social insects is low and optimization occurs largely at the group level. We review possible explanations for uneven workloads among workers, including limitations on individual information collection or constraints of task allocation efficiency, such as when there is a mismatch between the frequency of fluctuations in demand for work and the speed at which workers can be reallocated. These processes are likely to apply to any system in which worker agents are allocated to tasks with fluctuating demand, and should therefore be instructive to understanding optimal task allocation and inactive workers in any distributed system. Some of these processes imply that a certain proportion of inactive workers can be an adaptive strategy for collective organization.
KeywordsTask allocation Inactivity Organization of work Decentralized complex systems Social insects Resource allocation
- Bertsekas, D. P., & Tsitsiklis, J. N. (1989). Parallel and distributed computation: Numerical methods. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.Google Scholar
- Bolton, B., Alpert, G., Ward, P. S., & Naskrecki, P. (2006). Bolton’s catalogue of ants of the world: 1758–2005. Cambridge, MA: Harvard University Press.Google Scholar
- Bourke, A. F. G., & Franks, N. R. (1995). Social evolution in ants. Princeton, NJ: Princeton University Press.Google Scholar
- Breed, M. D., Guzmán-Novoa, E., & Hunt, G. J. (2004). Defensive behavior of honey bees: Organization, genetics, and comparisons with other bees. Annual Review of Entomology, 49(271–298), 3.Google Scholar
- Brugiavini, A., Croda, E., & Mariuzzo, F. (2005). Labour force participation of the elderly: Unused capacity? In A. Börsch-Supan, A. Brugiavini, H. Jürges, J. Mackenbach, J. Siegrist & G. Weber (Eds.), Health, ageing and retirement in Europe–first results from the survey of health, ageing and retirement in Europe (pp. 236–240). Mannheim: MEA.Google Scholar
- Camazine, S., Deneubourg, J. L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2003). Self-organization in biological systems. Princeton, NJ: Princeton University Press.Google Scholar
- Charbonneau, D., & Dornhaus, A. (2015). Workers “specialized” on inactivity: Behavioral consistency of inactive workers and their role in task allocation. Behavioral Ecology and Sociobiology. doi:10.1007/s00265-015-1958-1.
- Charbonneau, D., & Dornhaus, A. (In prep.). Who are the “lazy” ants? Concurrently testing multiple hypotheses for the function of inactivity in social insects.Google Scholar
- Charbonneau, D., Hillis, N., & Dornhaus, A. (2015). ‘Lazy’ in nature: Ant colony time budgets show high ‘inactivity’ in the field as well as in the lab. Insectes Sociaux, 62(1), 31–35. doi:10.1007/s00040-014-0370-6.
- Cornejo, A., Dornhaus, A., Lynch, N., & Nagpal, R. (2014). Task allocation in ant colonies. In F. Kuhn (Ed.), Distributed computing (pp. 46–60). Berlin: Springer.Google Scholar
- Di Caro, G., & Dorigo, M. (1998). AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research, 9, 317–365. http://arxiv.org/abs/1105.5449.
- Di Caro, G., Ducatelle, F., & Gambardella, L. M. (2004, January). AntHocNet: An ant-based hybrid routing algorithm for mobile ad hoc networks. In X. Yao, E. K. Burke, J. A. Lozano, J. Smith, J. J. Merelo-Guervós, J. A. Bullinaria, et al. (Eds.), Parallel Problem Solving from Nature—PPSN VIII (pp. 461–470). Berlin: Springer.Google Scholar
- Feinerman, O., & Korman, A. (2013). Theoretical distributed computing meets biology: A review. In C. Hota & P. K. Srimani (Eds.), Distributed computing and internet technology (pp. 1–18). Berlin: Springer.Google Scholar
- Foukia, N., Hassas, S., Fenet, S., & Albuquerque, P. (2003). Combining immune systems and social insect metaphors: a paradigm for distributed intrusion detection and response system. In E. Horlait, T. Magedanz & R. H. Glitho (Eds.), Mobile Agents for Telecommunication Applications for Telecommunication Applications (pp. 251–264). Berlin: Springer.Google Scholar
- Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., et al. (2009). Above the clouds: A Berkeley view of cloud computing. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Report UCB/EECS, 28, 13.Google Scholar
- Gadau, J., Fewell, J., & Wilson, E. O. (2009). Organization of insect societies: From genome to sociocomplexity. Cambridge, MA: Harvard University Press.Google Scholar
- Grimaldi, D. A., & Engel, M. S. (2005). Evolution of the insects. New York: Cambridge University Press.Google Scholar
- Hamermesh, D. S. (1990). Shirking or productive schmoozing: Wages and the allocation of time at work. Cambridge, MA: National Bureau of Economic Research.Google Scholar
- Hillis, N., Charbonneau, D., & Dornhaus, A. (In prep.). Are “lazy” ants selfish? Testing whether inactive ant workers invest more in their own reproduction.Google Scholar
- Itoh, H. (1992). Journal of Law, Economics, & Organization. Cooperation in hierarchical organizations: An incentive perspective, 8, 321–345.Google Scholar
- Johnson, S. (2012). Emergence: The connected lives of ants, brains, cities, and software. New York: Simon and Schuster.Google Scholar
- Kauffman, S. A. (1993). The origins of order: Self organization and selection in evolution. New York: Oxford University Press.Google Scholar
- Lanan, M. C., Dornhaus, A., Jones, E. I., Waser, A., & Bronstein, J. L. (2012). The trail less traveled: Individual decision-making and its effect on group behavior. PLoS One, 7(10), e47976. doi:10.1371/journal.pone.0047976.
- Levchuk, G. M., Levchuk, Y. N., Luo, J., Pattipati, K. R., & Kleinman, D. L. (2002). Normative design of organizations. I. Mission planning. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 32(3), 346–359. doi:10.1109/TSMCA.2002.802819.
- Lindauer, M. (1952). Ein beitrag zur frage der arbeitsteilung im bienenstaat. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 34, 299–345.Google Scholar
- Michener, C. D. (2000). The bees of the world. Baltimore, MD: JHU Press.Google Scholar
- Oldroyd, B. P., & Thompson, G. J. (2006). Behavioural genetics of the honey bee Apis mellifera. In S. J. Simpson (Ed.), Advances in insect physiology (pp. 1–49). London: Academic Press.Google Scholar
- Oster, G. F., & Wilson, E. O. (1978). Caste and ecology in the social insects. Princeton, NJ: Princeton University Press.Google Scholar
- Page, R. E., Jr, & Peng, C. Y.-S. (2001). Aging and development in social insects with emphasis on the honey bee, Apis mellifera L. Experimental Gerontology, 36, 695–711. doi:10.1016/S0531-5565(00)00236-9.
- Pankiw, T., & Page, R. E., Jr. (1999). he effect of genotype, age, sex, and caste on response thresholds to sucrose and foraging behavior of honey bees (Apis mellifera L.). Journal of Comparative Physiology A, 185, 207–213. doi:10.1007/s003590050379.
- Pankiw, T., & Page, R. E., Jr. (2001). Brood pheromone modulates honeybee (Apis mellifera L.) sucrose response thresholds. Behavioral Ecology and Sociobiology, 49, 206–213. doi:10.1007/s002650000282.
- Pankiw, T., Page, R. E., Jr, & Fondrk, M. K. (1998). Brood pheromone stimulates pollen foraging in honey bees (Apis mellifera). Behavioral Ecology and Sociobiology, 44, 193–198. doi:10.1007/s002650050531.
- Seeley, T. D. (2009). The wisdom of the hive: The social physiology of honey bee colonies. New York: Harvard University Press.Google Scholar
- Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. A. and C. Black.Google Scholar
- Varia, J. (2010). Architecting for the cloud: Best practices. http://jineshvaria.s3.amazonaws.com/public/cloudbestpractices-jvaria.pdf.
- Waibel, M., Floreano, D., Magnenat, S., & Keller, L. (2006). Division of labour and colony efficiency in social insects: Effects of interactions between genetic architecture, colony kin structure and rate of perturbations. Proceedings of the Royal Society B: Biological Sciences, 273, 1815–1823.CrossRefGoogle Scholar
- Waser, N. M., Chittka, L., Price, M. V., Williams, N. M., & Ollerton, J. (1996). Generalization in pollination systems, and why it matters. Ecology, 77(4), 1043–1060. doi:10.2307/2265575.
- Winston, M. L. (1991). The biology of the honey bee. New York: Harvard University Press.Google Scholar
- Zepelin, H. (1994). Mammalian sleep. In S. H. Sheldon, M. H. Kryger, R. Ferber & D. Gozal (Eds.), Principles and Practice of sleep medicine. Darien, IL: The American Academy of Sleep Medicine (AASM)Google Scholar