scholarly journals The effect of individual learning on collective foraging in honey bees in complex environments

2019 ◽  
Author(s):  
Natalie J. Lemanski ◽  
Chelsea N. Cook ◽  
Cahit Ozturk ◽  
Brian H. Smith ◽  
Noa Pinter-Wollman
Keyword(s):  
Collective Behavior ◽  
Honey Bees ◽  
Individual Learning ◽  
Centralized Control ◽  
Complex Environments ◽  
Agent Based ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Collective Foraging ◽  
Resource Patches

AbstractThe trade-off between exploiting known resources and exploring for new ones is a complex decision-making challenge, particularly when resource patches are variable in quality and heterogeneously distributed in the landscape. Social insect colonies navigate this challenge, in the absence of centralized control, by allocating different individuals to each of these tasks based on variation in individual behavior. To investigate how heritable differences in individual learning affect a colony’s collective ability to locate and choose among different quality food resources, we develop an agent based model and test its predictions using two genetic lines of honey bees, selected for differences in their learning behavior. Here we show that, paradoxically, colonies containing individuals that are better at learning to ignore unrewarding stimuli are worse at choosing the highest quality resource at the collective level. This work highlights the importance of individual variation within groups on the emergence of collective behavior.

scholarly journals A Multiscale Review of Behavioral Variation in Collective Foraging Behavior in Honey Bees

Insects ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 370 ◽  
Author(s):  
Natalie J. Lemanski ◽  
Chelsea N. Cook ◽  
Brian H. Smith ◽  
Noa Pinter-Wollman
Keyword(s):  
Individual Differences ◽  
Foraging Behavior ◽  
Collective Behavior ◽  
Individual Variation ◽  
Honey Bees ◽  
Multiple Scales ◽  
Model System ◽  
Bee Foraging ◽  
Collective Foraging ◽  
The Impact

The emergence of collective behavior from local interactions is a widespread phenomenon in social groups. Previous models of collective behavior have largely overlooked the impact of variation among individuals within the group on collective dynamics. Honey bees (Apis mellifera) provide an excellent model system for exploring the role of individual differences in collective behavior due to their high levels of individual variation and experimental tractability. In this review, we explore the causes and consequences of individual variation in behavior for honey bee foraging across multiple scales of organization. We summarize what is currently known about the genetic, developmental, and neurophysiological causes of individual differences in learning and memory among honey bees, as well as the consequences of this variation for collective foraging behavior and colony fitness. We conclude with suggesting promising future directions for exploration of the genetic and physiological underpinnings of individual differences in behavior in this model system.

scholarly journals An agent-based approach to weighted decision making in the spatially and temporally variable South African Palaeoscape

2019 ◽  
Author(s):  
Colin D. Wren ◽  
Chloe Atwater ◽  
Kim Hill ◽  
Marco Janssen ◽  
Jan de Vynck ◽  
...  
Keyword(s):  
Decision Making ◽  
South African ◽  
Optimal Foraging Theory ◽  
Middle Stone Age ◽  
Agent Based ◽  
Modelling Framework ◽  
Future Return ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Human Foraging

Even a simple human foraging system has a large number of moving parts. Foragers require a complex decision making process to effectively exploit the spatially and temporally variable resources in an environment. Here we present an agent-based modelling framework, based in optimal foraging theory, for agent foragers to make mobility and foraging decisions by weighing expected caloric returns against geographic and social factors, and forecasted future return rates. We apply our Paleoscape model to a spatially explicit South African coastal landscape to better understand the human foraging system of the Middle Stone Age, when foragers began systematically exploiting a wide variety of flora and fauna in both terrestrial and inter-tidal environments. We also discuss the broader importance of agent-based models of foraging systems for a wide variety of archaeological research questions.

scholarly journals Efficient agent-based negotiation by predicting opponent preferences using AHP

2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Usha Kiruthika ◽  
Thamarai Selvi Somasundaram
Keyword(s):  
Service Level ◽  
Analytic Hierarchy ◽  
Agent Based ◽  
Preference Information ◽  
Wide Range ◽  
Private Preference ◽  
Complex Decision Making ◽  
Optimal Line ◽  
Complex Decision ◽  
Hierarchy Process

Negotiation is a process essential for a wide range of applications. The complex decision making involved in negotiation makes its automation difficult. The complexity is further increased as negotiators hide their individual preferences from each other to avoid exploitation by the opponent. Even though sharing of private preference information leads to better agreement for both sides, it is never done in the absence of trust. In this work, we learn opponent’s preference information from the offers given by the opponent using Analytic Hierarchy Process (AHP). We apply our approach to the negotiation of Quality-of-Service (QoS) parameters for the establishment of Service Level Agreements (SLA) between a provider and a consumer. Experiments show that using AHP, the negotiations are faster and the agreements are on or nearer to the pareto-optimal line.

scholarly journals The effect of individual learning on collective foraging in honey bees in differently structured landscapes

2021 ◽  
Vol 179 ◽  
pp. 113-123
Author(s):  
Natalie J. Lemanski ◽  
Chelsea N. Cook ◽  
Cahit Ozturk ◽  
Brian H. Smith ◽  
Noa Pinter-Wollman
Keyword(s):  
Honey Bees ◽  
Individual Learning ◽  
Collective Foraging

A Tactical Game for the Analysis of Complex Decision Making in Individuals and Groups

1965 ◽  
Vol 17 (3) ◽  
pp. 723-729 ◽  
Author(s):  
Siegfried Streufert ◽  
M. A. Clardy ◽  
M. J. Driver ◽  
Marvin Karlins ◽  
H. M. Schroder ◽  
...  
Keyword(s):  
Measurement Techniques ◽  
Experimental Simulation ◽  
Human Information Processing ◽  
Complex Environments ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Game Environment ◽  
Perceptual Characteristics ◽  
Space Requirements ◽  
Analysis Of Performance

An experimental simulation is described as a prototype for research concerned with the analysis of human information processing characteristics in complex environments. The method employs a simulated war game environment which lends itself to the analysis of performance and perceptual characteristics of individuals and social groups. Suggestions for procedures, space requirements and measurement techniques are included.

scholarly journals Efficient agent-based negotiation by predicting opponent preferences using AHP

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Usha Kiruthika ◽  
Thamarai Selvi Somasundaram
Keyword(s):  
Service Level ◽  
Analytic Hierarchy ◽  
Agent Based ◽  
Preference Information ◽  
Wide Range ◽  
Private Preference ◽  
Complex Decision Making ◽  
Optimal Line ◽  
Complex Decision ◽  
Hierarchy Process

Negotiation is a process essential for a wide range of applications. The complex decision making involved in negotiation makes its automation difficult. The complexity is further increased as negotiators hide their individual preferences from each other to avoid exploitation by the opponent. Even though sharing of private preference information leads to better agreement for both sides, it is never done in the absence of trust. In this work, we learn opponent’s preference information from the offers given by the opponent using Analytic Hierarchy Process (AHP). We apply our approach to the negotiation of Quality-of-Service (QoS) parameters for the establishment of Service Level Agreements (SLA) between a provider and a consumer. Experiments show that using AHP, the negotiations are faster and the agreements are on or nearer to the pareto-optimal line.

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