Saori Iwanaga

Considering Psychological Conditions in a Tsunami Evacuation Simulation

The Great East Japan Earthquake occurred at 14:46 JST on Friday, March 11, 2011. It was the most powerful earthquake to have hit Japan and was one of the five most powerful earthquakes in the world since modern recordkeeping began in 1900. The earthquake triggered an extremely destructive tsunami with waves of up to 40.5 m in height. In this paper, in preparation for a possible Nankai Trough earthquake, we created a multi-agent tsunami evacuation simulation in Kure city to analyze the evacuation process and determine solutions to any problems. More specifically, we focus on the psychological conditions of people in the disaster area. During times of emergency, it is said that people fall into a psychological condition where they fail to evacuate. Based on the simulation results, we can confirm that people under psychological conditions require more time to evacuate than those in a normal frame of mind. Thus, people need to know more about psychological conditions during times of disaster to evacuate safely and efficiently.

Decentralized decision networks and their emergent properties

The large-scale effects of locally interacting agents are called emergent properties. Emergent properties are often surprising because it can be hard to anticipate the full consequences of even simple forms of interaction. We show how the society gropes its way towards equilibrium in an imperfect world where agents are sensible but not perfectly rational. We observe phenomena such as how each agents action combines with the actions of others to produce the whole behavior, and some to unanticipated results. We also address a new framework for a decentralized decision system to and real-time dynamic decision-making in a highly interdependent environment. We especially provide a new architecture of decentralized decision networks with the high degree of survivability.

Agreement algorithm using the trial and error method at the macrolevel

The best-of-n problem (Valentini et al. in Front Robot AI 4(9):1–18, 2017) is one of the decision-making problems in which many robots (agents) select the best option among a set of n alternatives and are focused on the field of Swarm Robotics. Almost all of the previous studies focused on binary decision-making scenarios (

Influence of Partner Selection on Functional Differentiation:Emergence of Diversity by Isolated Interaction and Preference Change

n = 2

Collective Behavior in Cascade Model Depend on Turn of Choice

n=2) and could not be applied without any change in the case of

Contagion of evacuation decision making on real map

n> 2