Why do retired workers claim their social security benefits so early? A potential explanation based on the cumulative prospect theory

2019 ◽  
Vol 52 (5) ◽  
pp. 490-505
Author(s):  
Rui Guo ◽  
Wei Sun ◽  
Jianqiu Wang ◽  
Gang Xiao
Keyword(s):  
Social Security ◽  
Prospect Theory ◽  
Cumulative Prospect Theory ◽  
Potential Explanation ◽  
Social Security Benefits ◽  
Retired Workers

scholarly journals Proposed Changes to Social Security: An Analysis

2011 ◽  
Vol 4 (2) ◽  
Author(s):  
Robert E. Pritchard ◽  
Gregory C. Potter
Keyword(s):  
Social Security ◽  
Interest Rates ◽  
Baby Boomers ◽  
Next Generation ◽  
Government Programs ◽  
Future Demands ◽  
Social Security Benefits ◽  
Retired Workers

Some 48 million Americans are expected to collect around $518 billion in Social Security benefits during 2005.  Of these, about 70 percent are retired workers.  The ratio of workers covered by Social Security to retirees is approximately three to one but will decrease to about two to one in the next generation.  Furthermore, at present, there are significantly more Social Security taxes collected than benefits paid; the excess is spent to help fund other government programs.  With the Baby Boomers starting to collect benefits in 2008 and large federal deficits already threatening to push interest rates higher, providing for future Social Security funding is being addressed.  This paper explores existing and future demands expected to be placed on Social Security and possible changes that may be implemented to ensure its long-term viability.

Research on disruption management of urgent arrival in job shop with deteriorating effect

2021 ◽  
pp. 1-13
Author(s):  
Ning Tao ◽  
Duan Xiaodong ◽  
An Lu ◽  
Gou Tao
Keyword(s):  
Prospect Theory ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Cumulative Prospect Theory ◽  
Disruption Management ◽  
Particle Swarm Algorithm ◽  
Shop Scheduling ◽  
Management Method

A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified.

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