Thesis subject

Evaluate the Impact of Informative Leaders on Collective Decision-Making in Agricultural Applications Using Drone Teams (MSc)

Short description

In agricultural applications, the quality and timeliness of decisions are paramount for ensuring efficiency and sustainability. Leaders, or individuals with superior information, play a crucial role in guiding collective decisions. This thesis focuses on developing a multi-agent model to study the influence of these leaders on group decision-making processes in agriculture, specifically utilizing a team of drones. By simulating different leadership scenarios and information quality levels within a drone team, the research aims to uncover the mechanisms through which leaders can positively or negatively impact collective outcomes. The findings will offer valuable insights for enhancing decision-making frameworks in agricultural settings.


Objectives

  1. Identify key factors that influence the effectiveness of leaders in guiding collective decisions.
  2. Investigate how leaders affect decision performance under various decision-making strategies.
  3. Investigate how leaders affect decision performance under various interaction networks.

    Tasks

    The work in this master thesis entails:

    • Literature Review: Conduct a detailed review of existing research on multi-agent models, leadership influence in collective decision-making, drone technology in agriculture, and agricultural applications. Identify key factors and knowledge gaps.
    • Model Design: Develop a conceptual framework for the multi-agent model, defining the roles, behaviors, and interactions of drone agents, with a focus on leaders and their influence.
    • Simulation Development: Implement the multi-agent model using appropriate simulation software. Incorporate realistic agricultural scenarios and data to validate the model, focusing on drone team operations.
    • Scenario Analysis: Conduct simulations under various conditions to evaluate the impact of drone leaders with higher quality information on collective decision-making. Analyze how different levels of information quality and leader influence affect overall outcomes.
    • Analysis and Testing: Analyze the model through sensitivity analysis and test the model under different conditions.
    • Document the findings and provide recommendations for future research.

    Requirements

    • Required skills/knowledge: Programming skills and willingness to learn new algorithms and simulation tools.

    Contact person(s)

    Yara Khaluf (yara.khaluf@wur.nl)