Traditional infrastructure systems are often designed for predictable scenarios, making them vulnerable to disruption when real-world conditions change unexpectedly. This research addresses that limitation by introducing Kinship Infrastructure Design (KID), a bioinspired framework based on the kinship coefficient (Φ). Adapted from studies of genetic relatedness in eusocial insects, the kinship coefficient (Φ) measures similarity and diversity among system components by calculating the degree to which their functional traits—encoded as a synthetic genome—match. This research investigates whether a system design can achieve a “Goldilocks Zone” of functional diversity where systems achieve optimal adaptability and coordination under uncertainty.\"
Rather than relying on predicting exactly how the system might fail, KID uses kinship coefficients as a design tool to guide early resource allocation decisions in the early design stage. This approach allows systems to remain flexible and effective even under uncertain disaster scenarios. The framework is evaluated using two constructive simulations implemented with agent-based modeling.
The first case study applies KID to a simulated wildfire response scenario, and models firefighting agents such as helicopters, firetrucks, bulldozers, and construction crews. These are encoded with traits like speed and suppression capability to calculate kinship coefficients for different configurations.
The second case study applies KID to a simulated COVID-19 scenario, involving agents like delivery drivers, doctors, nurses, IT personnel, and households. Each is defined by attributes such as response time, capacity, and service reliability. This case investigates how kinship-guided configurations maintain critical services during a public health crisis.
Across both case studies, the highest performance occurred within the mid-range kinship coefficients (Φ ≈ 0.50–0.75). In this region, the wildfire system achieved its peak with 83.6 percent of forest saved, while the pandemic-response system reached its maximum with 92 percent of daily service demand met, demonstrating a shared “Goldilocks Zone” where diversity and cohesion are optimally balanced.
By applying kinship-based principles across different infrastructure design challenges, this research aims to demonstrate KID’s potential as a versatile and scalable method for designing resilient infrastructure systems in uncertain environments.
","abstract_html":"<p>Traditional infrastructure systems are often designed for predictable scenarios, making them vulnerable to disruption when real-world conditions change unexpectedly. This research addresses that limitation by introducing Kinship Infrastructure Design (KID), a bioinspired framework based on the kinship coefficient (Φ). Adapted from studies of genetic relatedness in eusocial insects, the kinship coefficient (Φ) measures similarity and diversity among system components by calculating the degree to which their functional traits—encoded as a synthetic genome—match. This research investigates whether a system design can achieve a “Goldilocks Zone” of functional diversity where systems achieve optimal adaptability and coordination under uncertainty."</p> <p>Rather than relying on predicting exactly how the system might fail, KID uses kinship coefficients as a design tool to guide early resource allocation decisions in the early design stage. This approach allows systems to remain flexible and effective even under uncertain disaster scenarios. The framework is evaluated using two constructive simulations implemented with agent-based modeling.</p> <p>The first case study applies KID to a simulated wildfire response scenario, and models firefighting agents such as helicopters, firetrucks, bulldozers, and construction crews. These are encoded with traits like speed and suppression capability to calculate kinship coefficients for different configurations.</p> <p>The second case study applies KID to a simulated COVID-19 scenario, involving agents like delivery drivers, doctors, nurses, IT personnel, and households. Each is defined by attributes such as response time, capacity, and service reliability. This case investigates how kinship-guided configurations maintain critical services during a public health crisis.</p> <p>Across both case studies, the highest performance occurred within the mid-range kinship coefficients (Φ ≈ 0.50–0.75). In this region, the wildfire system achieved its peak with 83.6 percent of forest saved, while the pandemic-response system reached its maximum with 92 percent of daily service demand met, demonstrating a shared “Goldilocks Zone” where diversity and cohesion are optimally balanced.</p> <p>By applying kinship-based principles across different infrastructure design challenges, this research aims to demonstrate KID’s potential as a versatile and scalable method for designing resilient infrastructure systems in uncertain environments.</p>","abstract_has_math":false,"creators":["Maysha, Fayruz"],"institution":null,"degree_name":"Master of Science in Electrical & Computer Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Electrical, Computer, Software, and Systems Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-02T08:00:00Z","date_published":"2025-12-02T08:00:00Z","updated_at":"2026-07-27T19:26:22Z","subjects":["Agent-Based Modeling; Event-Driven Simulation; Resource-Constrained Systems; Essential Service Dynamics; Behavioral Interaction Modeling; Environmental Mitigation Processes; System-Level Emergence","Systems Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/944","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Maysha, Fayruz"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical, Computer, Software, and Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Electrical & Computer Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Agent-Based Modeling; Event-Driven Simulation; Resource-Constrained Systems; Essential Service Dynamics; Behavioral Interaction Modeling; Environmental Mitigation Processes; System-Level Emergence","Systems Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/944"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Traditional infrastructure systems are often designed for predictable scenarios, making them vulnerable to disruption when real-world conditions change unexpectedly. This research addresses that limitation by introducing Kinship Infrastructure Design (KID), a bioinspired framework based on the kinship coefficient (Φ). Adapted from studies of genetic relatedness in eusocial insects, the kinship coefficient (Φ) measures similarity and diversity among system components by calculating the degree to which their functional traits—encoded as a synthetic genome—match. This research investigates whether a system design can achieve a “Goldilocks Zone” of functional diversity where systems achieve optimal adaptability and coordination under uncertainty.\"
Rather than relying on predicting exactly how the system might fail, KID uses kinship coefficients as a design tool to guide early resource allocation decisions in the early design stage. This approach allows systems to remain flexible and effective even under uncertain disaster scenarios. The framework is evaluated using two constructive simulations implemented with agent-based modeling.
The first case study applies KID to a simulated wildfire response scenario, and models firefighting agents such as helicopters, firetrucks, bulldozers, and construction crews. These are encoded with traits like speed and suppression capability to calculate kinship coefficients for different configurations.
The second case study applies KID to a simulated COVID-19 scenario, involving agents like delivery drivers, doctors, nurses, IT personnel, and households. Each is defined by attributes such as response time, capacity, and service reliability. This case investigates how kinship-guided configurations maintain critical services during a public health crisis.
Across both case studies, the highest performance occurred within the mid-range kinship coefficients (Φ ≈ 0.50–0.75). In this region, the wildfire system achieved its peak with 83.6 percent of forest saved, while the pandemic-response system reached its maximum with 92 percent of daily service demand met, demonstrating a shared “Goldilocks Zone” where diversity and cohesion are optimally balanced.
By applying kinship-based principles across different infrastructure design challenges, this research aims to demonstrate KID’s potential as a versatile and scalable method for designing resilient infrastructure systems in uncertain environments.
"]},{"key":"dc:title","label":"Title","values":["Kinship Infrastructure Design: Evaluating Diversity and Resilience in Emergency Response Systems Through Agent-Based Modeling"]}]}],"canonical_facts":{"dc:creator":["Maysha, Fayruz"],"dc:description.abstract":["Traditional infrastructure systems are often designed for predictable scenarios, making them vulnerable to disruption when real-world conditions change unexpectedly. This research addresses that limitation by introducing Kinship Infrastructure Design (KID), a bioinspired framework based on the kinship coefficient (Φ). Adapted from studies of genetic relatedness in eusocial insects, the kinship coefficient (Φ) measures similarity and diversity among system components by calculating the degree to which their functional traits—encoded as a synthetic genome—match. This research investigates whether a system design can achieve a “Goldilocks Zone” of functional diversity where systems achieve optimal adaptability and coordination under uncertainty.\"
Rather than relying on predicting exactly how the system might fail, KID uses kinship coefficients as a design tool to guide early resource allocation decisions in the early design stage. This approach allows systems to remain flexible and effective even under uncertain disaster scenarios. The framework is evaluated using two constructive simulations implemented with agent-based modeling.
The first case study applies KID to a simulated wildfire response scenario, and models firefighting agents such as helicopters, firetrucks, bulldozers, and construction crews. These are encoded with traits like speed and suppression capability to calculate kinship coefficients for different configurations.
The second case study applies KID to a simulated COVID-19 scenario, involving agents like delivery drivers, doctors, nurses, IT personnel, and households. Each is defined by attributes such as response time, capacity, and service reliability. This case investigates how kinship-guided configurations maintain critical services during a public health crisis.
Across both case studies, the highest performance occurred within the mid-range kinship coefficients (Φ ≈ 0.50–0.75). In this region, the wildfire system achieved its peak with 83.6 percent of forest saved, while the pandemic-response system reached its maximum with 92 percent of daily service demand met, demonstrating a shared “Goldilocks Zone” where diversity and cohesion are optimally balanced.
By applying kinship-based principles across different infrastructure design challenges, this research aims to demonstrate KID’s potential as a versatile and scalable method for designing resilient infrastructure systems in uncertain environments.
"],"dc:identifier":["https://commons.erau.edu/edt/944"],"dc:subject":["Agent-Based Modeling; Event-Driven Simulation; Resource-Constrained Systems; Essential Service Dynamics; Behavioral Interaction Modeling; Environmental Mitigation Processes; System-Level Emergence","Systems Engineering"],"dc:title":["Kinship Infrastructure Design: Evaluating Diversity and Resilience in Emergency Response Systems Through Agent-Based Modeling"],"thesis:degree_discipline":["Electrical, Computer, Software, and Systems Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Electrical & Computer Engineering"]},"updated_at":"2026-07-27T19:26:22Z"}