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This article discusses the concept of specialization with fixed thresholds in task allocation and division of labor. It highlights the limitations of fixed systems and proposes a variable threshold model that can overcome these limitations. The model takes into account factors such as age, learning, and forgetting, and allows for specialization within a population. The article also explores optimization strategies and the advantages of the variable threshold model over fixed systems.
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Task Allocation And Division of Labor (Variable Threshold) By Greg Wilke and Jason Osborn
Yesterday and Today • Discussed specialization with fixed thresholds. • Fixed systems have limits. • Variable systems can overcome limitations of fixed systems.
Limitations of Fixed Systems • Can’t handle genesis of task allocation, including temporal polyethism. • Can’t account for strong task specialization within castes
More Limitations • Only valid over sufficiently short time scales. • Recent experiments with honey bees show that age and/or learning affect task allocation.
Adjusting-Threshold Model • Same as before except thresholds now vary with age. • Threshold limits dependent on tasks performed. • Lowered when task is performed • Raised when task is not performed
Learning and forgetting ξ and φ are the coefficients of learning and forgetting, respectively
Accounting for tasks performed Let Xij be the fraction of time spent on task j.
What does it mean? • Specialization can occur within an initially Homogeneous population • Individuals become more specialized as they perform the same task • If for some reason the task cannot be or does not need to be performed, individuals re-specialize.
Optimization • Adjust the rate that an ant forgets information. • If φ<0.4, all specialists. • If φ>2, no specialists.
Major Advantages • Removal of Ants with lower thresholds alters the thresholds of higher ants. • Fixed systems cannot respond to the change in demand.