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Estimating a path through a map of decision making.

Brock WA, Bentley RA, O'Brien MJ, Caiado CC - PLoS ONE (2014)

Bottom Line: Studies of the evolution of collective behavior consider the payoffs of individual versus social learning.We have previously proposed that the relative magnitude of social versus individual learning could be compared against the transparency of payoff, also known as the "transparency" of the decision, through a heuristic, two-dimensional map.Moving from west to east, the estimated strength of social influence increases.

View Article: PubMed Central - PubMed

Affiliation: Department of Economics, University of Wisconsin, Madison, WI, United States of America and Department of Economics, University of Missouri, Columbia, MO, United States of America.

ABSTRACT
Studies of the evolution of collective behavior consider the payoffs of individual versus social learning. We have previously proposed that the relative magnitude of social versus individual learning could be compared against the transparency of payoff, also known as the "transparency" of the decision, through a heuristic, two-dimensional map. Moving from west to east, the estimated strength of social influence increases. As the decision maker proceeds from south to north, transparency of choice increases, and it becomes easier to identify the best choice itself and/or the best social role model from whom to learn (depending on position on east-west axis). Here we show how to parameterize the functions that underlie the map, how to estimate these functions, and thus how to describe estimated paths through the map. We develop estimation methods on artificial data sets and discuss real-world applications such as modeling changes in health decisions.

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Related in: MedlinePlus

Simulations of binary choice model with varying choice intensity  and social influence intensity .For clarity the plots only show the proportion of agents making one of the two choices (e.g., non-parent). The panels show 16 different combinations of  and , with  for all. Each panel shows results of simulation with 30 time steps, 100 groups and 200 agents per group, noise component  with mean 0 and , and starting proportion 80% for the choice shown (so the choice not shown starts at 20%). The payoffs  and  are chosen from , and  and  are both chosen from . Gray bounds show 95% quantiles for sample paths over group for the proportion of non-parents over groups.
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pone-0111022-g003: Simulations of binary choice model with varying choice intensity and social influence intensity .For clarity the plots only show the proportion of agents making one of the two choices (e.g., non-parent). The panels show 16 different combinations of and , with for all. Each panel shows results of simulation with 30 time steps, 100 groups and 200 agents per group, noise component with mean 0 and , and starting proportion 80% for the choice shown (so the choice not shown starts at 20%). The payoffs and are chosen from , and and are both chosen from . Gray bounds show 95% quantiles for sample paths over group for the proportion of non-parents over groups.

Mentions: The specification in equation 7 allows other variations that yield more novel results. To convey the effects of varying and , Figure 3 shows the change in behavior for a binary choice under different values of and (for clarity, Figure 3 shows just the proportion of one of the two choices). In varying these two parameters, we find variation not only in final outcomes after 30 time steps, but in the dynamics of choice as well (Figure 3). When is negative, for example, we move toward social independence, and, for positive , decisions tend to be made socially. Similarly, when is negative, we move south toward ambivalence, and, as increases, we move north toward a transparency between the binary options. If is low and high, a member (or a whole group) might be able to choose something different from the norm. This event, however, becomes rarer as social influence increases.


Estimating a path through a map of decision making.

Brock WA, Bentley RA, O'Brien MJ, Caiado CC - PLoS ONE (2014)

Simulations of binary choice model with varying choice intensity  and social influence intensity .For clarity the plots only show the proportion of agents making one of the two choices (e.g., non-parent). The panels show 16 different combinations of  and , with  for all. Each panel shows results of simulation with 30 time steps, 100 groups and 200 agents per group, noise component  with mean 0 and , and starting proportion 80% for the choice shown (so the choice not shown starts at 20%). The payoffs  and  are chosen from , and  and  are both chosen from . Gray bounds show 95% quantiles for sample paths over group for the proportion of non-parents over groups.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219699&req=5

pone-0111022-g003: Simulations of binary choice model with varying choice intensity and social influence intensity .For clarity the plots only show the proportion of agents making one of the two choices (e.g., non-parent). The panels show 16 different combinations of and , with for all. Each panel shows results of simulation with 30 time steps, 100 groups and 200 agents per group, noise component with mean 0 and , and starting proportion 80% for the choice shown (so the choice not shown starts at 20%). The payoffs and are chosen from , and and are both chosen from . Gray bounds show 95% quantiles for sample paths over group for the proportion of non-parents over groups.
Mentions: The specification in equation 7 allows other variations that yield more novel results. To convey the effects of varying and , Figure 3 shows the change in behavior for a binary choice under different values of and (for clarity, Figure 3 shows just the proportion of one of the two choices). In varying these two parameters, we find variation not only in final outcomes after 30 time steps, but in the dynamics of choice as well (Figure 3). When is negative, for example, we move toward social independence, and, for positive , decisions tend to be made socially. Similarly, when is negative, we move south toward ambivalence, and, as increases, we move north toward a transparency between the binary options. If is low and high, a member (or a whole group) might be able to choose something different from the norm. This event, however, becomes rarer as social influence increases.

Bottom Line: Studies of the evolution of collective behavior consider the payoffs of individual versus social learning.We have previously proposed that the relative magnitude of social versus individual learning could be compared against the transparency of payoff, also known as the "transparency" of the decision, through a heuristic, two-dimensional map.Moving from west to east, the estimated strength of social influence increases.

View Article: PubMed Central - PubMed

Affiliation: Department of Economics, University of Wisconsin, Madison, WI, United States of America and Department of Economics, University of Missouri, Columbia, MO, United States of America.

ABSTRACT
Studies of the evolution of collective behavior consider the payoffs of individual versus social learning. We have previously proposed that the relative magnitude of social versus individual learning could be compared against the transparency of payoff, also known as the "transparency" of the decision, through a heuristic, two-dimensional map. Moving from west to east, the estimated strength of social influence increases. As the decision maker proceeds from south to north, transparency of choice increases, and it becomes easier to identify the best choice itself and/or the best social role model from whom to learn (depending on position on east-west axis). Here we show how to parameterize the functions that underlie the map, how to estimate these functions, and thus how to describe estimated paths through the map. We develop estimation methods on artificial data sets and discuss real-world applications such as modeling changes in health decisions.

Show MeSH
Related in: MedlinePlus