scholarly journals Peer Effects on Real-Time Search Behavior in Experimental Stock Markets

2021 ◽  
Vol 12 ◽  
Author(s):  
Xuejun Jin ◽  
Xue Zhou ◽  
Xiaolan Yang ◽  
Yiyang Lin
Keyword(s):  
Financial Markets ◽  
Real Time ◽  
Search Efficiency ◽  
Feedback Information ◽  
Ex Post ◽  
Best Response ◽  
Search Behaviors ◽  
Real Time Information ◽  
Time Information ◽  
Similar Search

It is a well-documented phenomenon that individuals stop searching earlier than predicted by the optimal, risk-neutral stopping rule, leading to inefficient searches. Individuals' search behaviors during making investment decisions in financial markets can be easily affected by their peers. In this study, we designed a search game in a simplified experimental stock market in which subjects were required to search for the best sell prices for their stocks. By randomly assigning subjects into pairs and presenting them with real-time information on their peers' searches, we investigated the effects of peers' decisions on search behaviors. The results showed that two subjects in the same group with real-time peer information learned and engaged in similar search behaviors. However, this peer effect did not exist when subjects had access to feedback information on the ex-post best response. In addition, we found that the presence of information about peers' decisions alone had no significant impact on search efficiency, whereas access to both information on peers' decisions and feedback information significantly improved subjects' search efficiency.

scholarly journals Deep Reinforcement Learning for Green Security Games with Real-Time Information

2019 ◽  
Vol 33 ◽  
pp. 1401-1408 ◽  
Author(s):  
Yufei Wang ◽  
Zheyuan Ryan Shi ◽  
Lantao Yu ◽  
Yi Wu ◽  
Rohit Singh ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Law Enforcement Agencies ◽  
Local Modes ◽  
Response Strategies ◽  
Best Response ◽  
Heuristic Strategies ◽  
Security Games ◽  
Real Time Information ◽  
Time Information

Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing. However, real-time information such as footprints and agents’ subsequent actions upon receiving the information, e.g., rangers following the footprints to chase the poacher, have been neglected in previous work. To fill the gap, we first propose a new game model GSG-I which augments GSGs with sequential movement and the vital element of real-time information. Second, we design a novel deep reinforcement learning-based algorithm, DeDOL, to compute a patrolling strategy that adapts to the real-time information against a best-responding attacker. DeDOL is built upon the double oracle framework and the policy-space response oracle, solving a restricted game and iteratively adding best response strategies to it through training deep Q-networks. Exploring the game structure, DeDOL uses domain-specific heuristic strategies as initial strategies and constructs several local modes for efficient and parallelized training. To our knowledge, this is the first attempt to use Deep Q-Learning for security games.

scholarly journals Optimal Interdiction of Urban Criminals with the Aid of Real-Time Information

2019 ◽  
Vol 33 ◽  
pp. 1262-1269
Author(s):  
Youzhi Zhang ◽  
Qingyu Guo ◽  
Bo An ◽  
Long Tran-Thanh ◽  
Nicholas R. Jennings
Keyword(s):  
Real Time ◽  
Urban Network ◽  
The Real ◽  
Response Strategies ◽  
Best Response ◽  
Nest Model ◽  
Suburban Cities ◽  
Realistic Problem ◽  
Real Time Information ◽  
Time Information

Most violent crimes happen in urban and suburban cities. With emerging tracking techniques, law enforcement officers can have real-time location information of the escaping criminals and dynamically adjust the security resource allocation to interdict them. Unfortunately, existing work on urban network security games largely ignores such information. This paper addresses this omission. First, we show that ignoring the real-time information can cause an arbitrarily large loss of efficiency. To mitigate this loss, we propose a novel NEtwork purSuiT game (NEST) model that captures the interaction between an escaping adversary and a defender with multiple resources and real-time information available. Second, solving NEST is proven to be NP-hard. Third, after transforming the non-convex program of solving NEST to a linear program, we propose our incremental strategy generation algorithm, including: (i) novel pruning techniques in our best response oracle; and (ii) novel techniques for mapping strategies between subgames and adding multiple best response strategies at one iteration to solve extremely large problems. Finally, extensive experiments show the effectiveness of our approach, which scales up to realistic problem sizes with hundreds of nodes on networks including the real network of Manhattan.

Potential Urban Rainfall Prediction Measurement System

1984 ◽  
Vol 16 (8-9) ◽  
pp. 349-362 ◽  
Author(s):  
John L Vogel
Keyword(s):  
Real Time ◽  
The United States ◽  
Monitoring Systems ◽  
Climatic Region ◽  
Convective Storm ◽  
Rainfall Prediction ◽  
Water Quality Regulations ◽  
General Data ◽  
Real Time Information ◽  
Time Information

Continued growth of urban regions and more stringent water quality regulations have resulted in an increased need for more real-time information about past, present, and future patterns and intensities of precipitation. Detailed, real-time information about precipitation can be obtained using radar and raingages for monitoring and prediction of precipitation amounts. The philosophy and the requirements for the development of real-time radar prediction-monitoring systems are described for climatic region similar to the Midwest of the united States. General data analysis and interpretation techniques associated with rainfall from convective storm systems are presented.

Sign in / Sign up

Export Citation Format

Share Document