Self-organization of collective escape in pigeon flocks

Bird flocks under predation demonstrate complex patterns of collective escape. These patterns may emerge by self-organization from local interactions among group-members. Computational models have been shown to be valuable for identifying what behavioral rules may govern such interactions among individuals during collective motion. However, our knowledge of such rules for collective escape is limited by the lack of quantitative data on bird flocks under predation in the field. In the present study, we analyze the first GPS trajectories of pigeons in airborne flocks attacked by a robotic falcon in order to build a species-specific model of collective escape. We use our model to examine a recently identified distance-dependent pattern of collective behavior: the closer the prey is to the predator, the higher the frequency with which flock members turn away from it. We first extract from the empirical data of pigeon flocks the characteristics of their shape and internal structure (bearing angle and distance to nearest neighbors). Combining these with information on their coordination from the literature, we build an agent-based model adjusted to pigeons’ collective escape. We show that the pattern of turning away from the predator with increased frequency when the predator is closer arises without prey prioritizing escape when the predator is near. Instead, it emerges through self-organization from a behavioral rule to avoid the predator independently of their distance to it. During this self-organization process, we show how flock members increase their consensus over which direction to escape and turn collectively as the predator gets closer. Our results suggest that coordination among flock members, combined with simple escape rules, reduces the cognitive costs of tracking the predator while flocking. Such escape rules that are independent of the distance to the predator can now be investigated in other species. Our study showcases the important role of computational models in the interpretation of empirical findings of collective behavior.

How the Pandemic Made Sensory Power Visible

During the COVID-19 pandemic, three long-established forms of power—sovereign, disciplinary, and regulatory—have been conspicuously deployed around the world, as seen in lockdowns, quarantines, and behavioral rules. The pandemic has also revealed a fourth form of power: sensory power, which emerged with the rapid evolution of sensing and surveillance technologies. The data collected by tracking and tracing constitutes a planetary ecosystem for governing people. Whether this leads to digital dictatorships or digital democracies, the growth of sensory power will change the relationship between states and citizens in the twenty-first century.

The Reality of Disaster Mitigation Activities for Companies in Tokyo’s Central Business District: A Case Study on Support for Stranded Commuters in the Initial Period in Chuo-Ku

In this study, the Council of Policy for Stranded Commuters in Chuo-ku, Tokyo, is considered as an example of local disaster mitigation activities through mutual aid for companies. The process of the activities during the initial period is described, and the points necessary to “establish and maintain the disaster mitigation activities by multiple companies” are summarized. The efforts of the council have led to the development of a community in which various disaster mitigation measures are not limited to those for stranded commuters but include responses to the sick and wounded; these disaster mitigation measures within companies are discussed. As a result, four points are derived as important factors, namely, grasping the local characteristics, forming an organization where the consensus can be built smoothly together with administrative bodies and academics, formulating local behavioral rules, and stipulating the principles behind the activities and the future prospects. The efforts for the local disaster mitigation activities taken up mainly by the residents of communities have been reported in many studies. However, there are few studies on the disaster mitigation activities conducted for companies; as such, only a case study of the measures for stranded commuters in Shinjuku-ku, Tokyo has been reported [1]. This is a case study that reports the process of the establishment of disaster mitigation activities for companies; based on the case study of Shinjuku-ku, Tokyo, this study takes into consideration the characteristics of Chuo-ku, Tokyo, which has no terminal station.

A Network Evolution Model of Credit Risk Contagion between Banks and Enterprises Based on Agent-Based Model

Credit risk contagion between banks and firms is one of the important triggers of financial crisis, and the credit linkage network is the way of systemic risk contagion triggered by external shocks. Considering the heterogeneity of behavioral rules, learning rules, and interaction rules, this paper constructs a bank-firm credit matching network model based on ABM (agent-based model) model and reinforcement learning algorithm to analyze the interaction behavior and credit risk network contagion mechanism. The results show that (1) macroeconomic cycles are the result of the interaction between banks and enterprises and the interaction of microentities under complex financial conditions; (2) enterprises are heterogeneous and the asset size follows a power-law distribution; (3) the greater the sensitivity of banks and enterprises to market performance, the lower the bank failure rate and enterprise default rate; and (4) shocks to the largest banks and enterprises in terms of assets and entry can all intensify the risk contagion between banks and enterprises. Therefore, the regulation of financial institutions that are “too big to fail” is not sufficient but should be a comprehensive regulation of the banking system.

Intention modelling and inference for autonomous collision avoidance at sea

The open wording of the traffic rules of the sea, COLREGS, and the existence of unwritten rules, make it essential for an autonomous ship to understand the intentions of meeting traffic. This article uses a dynamic Bayesian network (DBN) to model and infer the intentions of other ships based on their observed real-time behavior. Multiple intention nodes are included to describe the different ways a ship can interpret and conflict with the behavioral rules outlined in CORLEGS. The prior distributions of the intention nodes are adapted to the current situation based on observable characteristics such as location and relative ship size. When a new observation is made, the probability distributions of the intention variables are updated by excluding all combinations of intention states that conflict with the observed behavior. This way of modeling makes the intention probabilities independent of how often observations are made. The resulting model is able to identify situations that are prone to cause misunderstandings and infer the state of multiple intention variables that describe the behavior. Different collision avoidance algorithms can use the resulting intention information to better know if, when, and how to act.

Intention modelling and inference for autonomous collision avoidance at sea

The open wording of the traffic rules of the sea, COLREGS, and the existence of unwritten rules, make it essential for an autonomous ship to understand the intentions of meeting traffic. This article uses a dynamic Bayesian network (DBN) to model and infer the intentions of other ships based on their observed real-time behavior. Multiple intention nodes are included to describe the different ways a ship can interpret and conflict with the behavioral rules outlined in CORLEGS. The prior distributions of the intention nodes are adapted to the current situation based on observable characteristics such as location and relative ship size. When a new observation is made, the probability distributions of the intention variables are updated by excluding all combinations of intention states that conflict with the observed behavior. This way of modeling makes the intention probabilities independent of how often observations are made. The resulting model is able to identify situations that are prone to cause misunderstandings and infer the state of multiple intention variables that describe the behavior. Different collision avoidance algorithms can use the resulting intention information to better know if, when, and how to act.

THE BEHAVIOR OF THE SOIL UNDER FOUNDATION OF OFFSHORE GRAVITY STRUCTURES SUBJECTED TO DIFFERENT COMBINATION OF LOADS

This study aims at analyzing the behavioral rules of the soil under the bottom of the offshore gravity structures when it had been subjected to the combination of load types; in which the problem of determining the deformation of the ground in semi-infinite space is determined by the view of deformed solid mechanics and elastic theory. The ground plane displacement of the soil is simulated by the computer when the structural system is subjected to concentrated loads and the distribution load is consist of the eccentricity due to wave load and other horizontal loads.

A Reliable Behavioral Model

This study concerns a swarm of autonomous reactive mobile robots, qualified of naïve because of their simple constitution, having the mission of gathering objects randomly distributed while respecting two contradictory objectives: maximizing quality of the emergent heap-formation and minimizing energy consumed by aforesaid robots. This problem poses two challenges: it is a multi-objective optimization problem and it is a hard problem. To solve it, one of renowned multi-objective evolutionary algorithms is used. Obtained solution, via a simulation process, unveils a close relationship between behavioral-rules and consumed energy; it represents the sought behavioral model, optimizing the grouping quality and energy consumption. Its reliability is shown by evaluating its robustness, scalability, and flexibility. Also, it is compared with a single-objective behavioral model. Results' analysis proves its high robustness, its superiority in terms of scalability and flexibility, and its longevity measured based on the activity time of the robotic system that it integrates.

Integrating Behavioral Theories in Agent-Based Models for Agricultural Drought Risk Assessments

Improving assessments of droughts risk for smallholder farmers requires a better understanding of the interaction between individual adaptation decisions and drought risk. Agent-based modeling is increasingly used to capture the interaction between individual decision-making and the environment. In this paper, we provide a review of drought risk agent-based models with a focus on behavioral rules. This review leads to the conclusion that human decision rules in existing drought risk agent-based models are often based on ad hoc assumptions without a solid theoretical and empirical foundation. Subsequently, we review behavioral economic and psychological theories to provide a clear overview of theories that can improve the theoretical foundation of smallholder farmer behavior and we review empirical parameterization, calibration, and validation methods of those theories. Based on these reviews, we provide a conceptual framework that can give guidance for the integration of behavioral theories in agent-based models. We conclude with an agenda to guide future research in this field.

Self-organization of collective escape in pigeon flocks

Bird flocks under predation demonstrate complex patterns of collective escape. These patterns may emerge by self-organization from simple interactions among group-members. Computational models have been shown to be valuable for identifying the behavioral rules that may govern these interactions among individuals during collective motion. However, our knowledge of such rules for collective escape is limited by the lack of quantitative data on bird flocks under predation in the field. In the present study, we analyze the first dataset of GPS trajectories of pigeons in airborne flocks attacked by a robotic falcon in order to build a species-specific model of collective escape. We use our model to examine a recently identified distance-dependent pattern of collective behavior that shows an increase in the escape frequency of pigeons when the predator is closer. We first extract from the empirical data the characteristics of pigeon flocks regarding their shape and internal structure (bearing angle and distance to nearest neighbours). Combining these with information on their coordination from the literature, we build an agent-based model tuned to pigeons' collective escape. We show that the pattern of increased escape frequency closer to the predator arises without flock-members prioritizing escape when the predator is near. Instead, it emerges through self-organization from an individual rule of predator-avoidance that is independent of predator-prey distance. During this self-organization process, we uncover a role of hysteresis and show that flock members increase their consensus over the escape direction and turn collectively as the predator gets closer. Our results suggest that coordination among flock-members, combined with simple escape rules, reduces the cognitive costs of tracking the predator. Such rules that are independent of predator-prey distance can now be examined in other species. Finally, we emphasize on the important role of computational models in the interpretation of empirical findings of collective behavior.