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Granovskiy, Boris
Publications (6 of 6) Show all publications
Granovskiy, B., Gold, J. M., Sumpter, D. J. T. & Goldstone, R. L. (2015). Integration of Social Information by Human Groups. Topics in Cognitive Science, 7(3), 469-493
Open this publication in new window or tab >>Integration of Social Information by Human Groups
2015 (English)In: Topics in Cognitive Science, ISSN 1756-8757, E-ISSN 1756-8765, Vol. 7, no 3, p. 469-493Article in journal (Refereed) Published
Abstract [en]

We consider a situation in which individuals search for accurate decisions without direct feedback on their accuracy, but with information about the decisions made by peers in their group. The wisdom of crowds hypothesis states that the average judgment of many individuals can give a good estimate of, for example, the outcomes of sporting events and the answers to trivia questions. Two conditions for the application of wisdom of crowds are that estimates should be independent and unbiased. Here, we study how individuals integrate social information when answering trivia questions with answers that range between 0% and 100% (e.g., What percentage of Americans are left-handed?). We find that, consistent with the wisdom of crowds hypothesis, average performance improves with group size. However, individuals show a consistent bias to produce estimates that are insufficiently extreme. We find that social information provides significant, albeit small, improvement to group performance. Outliers with answers far from the correct answer move toward the position of the group mean. Given that these outliers also tend to be nearer to 50% than do the answers of other group members, this move creates group polarization away from 50%. By looking at individual performance over different questions we find that some people are more likely to be affected by social influence than others. There is also evidence that people differ in their competence in answering questions, but lack of competence is not significantly correlated with willingness to change guesses. We develop a mathematical model based on these results that postulates a cognitive process in which people first decide whether to take into account peer guesses, and if so, to move in the direction of these guesses. The size of the move is proportional to the distance between their own guess and the average guess of the group. This model closely approximates the distribution of guess movements and shows how outlying incorrect opinions can be systematically removed from a group resulting, in some situations, in improved group performance. However, improvement is only predicted for cases in which the initial guesses of individuals in the group are biased.

National Category
Psychology
Identifiers
urn:nbn:se:uu:diva-180969 (URN)10.1111/tops.12150 (DOI)000359784900007 ()26189568 (PubMedID)
Available from: 2012-09-14 Created: 2012-09-14 Last updated: 2017-12-07Bibliographically approved
Granovskiy, B., Latty, T., Duncan, M., Sumpter, D. J. T. & Beekman, M. (2012). How dancing honey bees keep track of changes: the role of inspector bees. Behavioral Ecology, 23(3), 588-596
Open this publication in new window or tab >>How dancing honey bees keep track of changes: the role of inspector bees
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2012 (English)In: Behavioral Ecology, ISSN 1045-2249, E-ISSN 1465-7279, Vol. 23, no 3, p. 588-596Article in journal (Refereed) Published
Abstract [en]

How do honey bees track changes in their foraging environment? Previously, 2 complementary mechanisms have been identified by which bees can effectively switch between food sources when their relative quality changes. First, an increase in profitability of a food source elicits an increase in waggle dances (the bees' recruitment mechanism) for that source. Second, bees that have retired from foraging at a food source make occasional inspection visits to that food source and resume foraging if its quality improves. Here, we investigate, using both field experiments and a mathematical model, the relative importance of these 2 mechanisms. By manipulating dance information available to the bees, we find that when food sources change quality frequently, inspector bees provide a rapid response to changes, whereas the waggle dance contributes to a response over a longer time period. The bees' ability to switch feeders without dance language information was found to be robust with respect to the spatial configuration of the feeders. Our results show that individual memory, in the form of inspector bees, and collective communication can interact to allow an insect colony to adapt to changes on both short and long timescales.

Keywords
Apis mellifera, collective memory, dynamic environment, foraging, honey bee, mathematical modeling
National Category
Mathematics Biological Sciences
Identifiers
urn:nbn:se:uu:diva-173619 (URN)10.1093/beheco/ars002 (DOI)000302485200017 ()
Available from: 2012-05-09 Created: 2012-05-02 Last updated: 2018-05-29Bibliographically approved
Perna, A., Granovskiy, B., Garnier, S., Nicolis, S. C., Labédan, M., Theraulaz, G., . . . Sumpter, D. J. T. (2012). Individual Rules for Trail Pattern Formation in Argentine Ants (Linepithema humile). PloS Computational Biology, 8(7), e1002592
Open this publication in new window or tab >>Individual Rules for Trail Pattern Formation in Argentine Ants (Linepithema humile)
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2012 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 8, no 7, p. e1002592-Article in journal (Refereed) Published
Abstract [en]

Many ant species produce large dendritic networks of trails around their nest. These networks result from self-organized feedback mechanisms: ants leave small amounts of a chemical -a pheromone- as they move across space. In turn, they are attracted by this same pheromone so that eventually a trail is formed. In our study, we introduce a new image analysis technique to estimate the concentrations of pheromone directly on the trails. In this way, we can characterise the ingredients of the feedback loop that ultimately leads to the formation of trails. We show that the response to pheromone concentrations is linear: an ant will turn to the left with frequency proportional to the difference between the pheromone concentrations on its left and right sides. Such a linear individual response was rejected by previous literature, as it would be incompatible with the results of a large number of experiments: trails can only be reinforced if the ants have a disproportionally higher probability to select the trail with higher pheromone concentration. However, we show that the required non-linearity does not reside in the perceptual response of the ants, but in the noise associated with their movement.

National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-180968 (URN)10.1371/journal.pcbi.1002592 (DOI)000306842200017 ()
Available from: 2012-09-14 Created: 2012-09-14 Last updated: 2017-12-07Bibliographically approved
Granovskiy, B. (2012). Modeling Collective Decision-Making in Animal Groups. (Doctoral dissertation). Uppsala: Department of Mathematics
Open this publication in new window or tab >>Modeling Collective Decision-Making in Animal Groups
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many animal groups benefit from making decisions collectively. For example, colonies of many ant species are able to select the best possible nest to move into without every ant needing to visit each available nest site. Similarly, honey bee colonies can focus their foraging resources on the best possible food sources in their environment by sharing information with each other. In the same way, groups of human individuals are often able to make better decisions together than each individual group member can on his or her own. This phenomenon is known as "collective intelligence", or "wisdom of crowds." What unites all these examples is the fact that there is no centralized organization dictating how animal groups make their decisions. Instead, these successful decisions emerge from interactions and information transfer between individual members of the group and between individuals and their environment. In this thesis, I apply mathematical modeling techniques in order to better understand how groups of social animals make important decisions in situations where no single individual has complete information. This thesis consists of five papers, in which I collaborate with biologists and sociologists to simulate the results of their experiments on group decision-making in animals. The goal of the modeling process is to better understand the underlying mechanisms of interaction that allow animal groups to make accurate decisions that are vital to their survival. Mathematical models also allow us to make predictions about collective decisions made by animal groups that have not yet been studied experimentally or that cannot be easily studied. The combination of mathematical modeling and experimentation gives us a better insight into the benefits and drawbacks of collective decision making, and into the variety of mechanisms that are responsible for collective intelligence in animals. The models that I use in the thesis include differential equation models, agent-based models, stochastic models, and spatially explicit models. The biological systems studied included foraging honey bee colonies, house-hunting ants, and humans answering trivia questions.

Place, publisher, year, edition, pages
Uppsala: Department of Mathematics, 2012. p. 49
Series
Uppsala Dissertations in Mathematics, ISSN 1401-2049 ; 78
Keywords
collective animal behavior, collective intelligence, swarm intelligence, wisdom of crowds, mathematical modeling, stochastic modeling, agent-based modeling, Markov chain models
National Category
Mathematics Behavioral Sciences Biology
Identifiers
urn:nbn:se:uu:diva-180972 (URN)
Public defence
2012-10-26, Häggsalen, Ångström Laboratory, Lägerhyddsvägen 1, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2012-10-05 Created: 2012-09-14 Last updated: 2012-10-05Bibliographically approved
Sasaki, T., Granovskiy, B., Mann, R. P., Sumpter, D. J. T. & Pratt, S. C.Task difficulty  determines whether or not a crowd is wise..
Open this publication in new window or tab >>Task difficulty  determines whether or not a crowd is wise.
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(English)Manuscript (preprint) (Other academic)
National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-180970 (URN)
Available from: 2012-09-14 Created: 2012-09-14
Granovskiy, B.Testing an agent-based model of nest emigration in Temnothorax ants against new experimental data..
Open this publication in new window or tab >>Testing an agent-based model of nest emigration in Temnothorax ants against new experimental data.
(English)Manuscript (preprint) (Other academic)
National Category
Natural Sciences
Identifiers
urn:nbn:se:uu:diva-180971 (URN)
Available from: 2012-09-14 Created: 2012-09-14
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