scholarly journals Regret in Experience-Based Decisions: The Effects of Expected Value Differences and Mixed Gains and Losses

2020 ◽  
Author(s):  
William M. Hayes ◽  
Douglas Wedell
Keyword(s):  
Reinforcement Learning ◽  
Structural Features ◽  
Expected Value ◽  
Online Experiment ◽  
Value Differences ◽  
Mixed Outcomes ◽  
Relative Comparisons ◽  
Gains And Losses ◽  
The Impact

Previous research on experience-based decisions with full feedback supports the idea that people tend to prefer options that minimize the probability of regret. The current study explored whether this preference is modulated by differences in expected value (EV) and the presence or absence of occasional losses. Participants (n = 52) completed an online experiment that involved repeated choices between a safer and a riskier option while receiving full feedback. The riskier option yielded a better outcome on 80% of draws so that choosing it minimized the probability of regret. Preference for the riskier, regret-minimizing option was high when it had the same EV as the safer option and all outcomes were gains, but it decreased when the safer option had a higher EV and when both options included occasional losses. Outcome ratings that were obtained on 50% of trials showed large effects of regret and rejoicing, confirming that participants were sensitive to relative comparisons between obtained and forgone outcomes. Reinforcement-learning modeling indicated that the effects of unequal EVs and mixed outcomes could be accounted for by assuming combined encoding of absolute and relative outcomes and unequal weighting of gains and losses. Overall, these results demonstrate that the impact of regret can be modulated by structural features of the choice environment.

Exploring Deep Reinforcement Learning for Task Dispatching in Autonomous On-Demand Services

2021 ◽  
Vol 15 (3) ◽  
pp. 1-23
Author(s):  
Lei Yang ◽  
Xi Yu ◽  
Jiannong Cao ◽  
Xuxun Liu ◽  
Pan Zhou
Keyword(s):  
Reinforcement Learning ◽  
Response Time ◽  
Expected Value ◽  
Future Perspective ◽  
Response Patterns ◽  
On Demand ◽  
The Future ◽  
Future Demands ◽  
Real World Datasets ◽  
The Impact

Autonomous on-demand services, such as GOGOX (formerly GoGoVan) in Hong Kong, provide a platform for users to request services and for suppliers to meet such demands. In such a platform, the suppliers have autonomy to accept or reject the demands to be dispatched to him/her, so it is challenging to make an online matching between demands and suppliers. Existing methods use round-based approaches to dispatch demands. In these works, the dispatching decision is based on the predicted response patterns of suppliers to demands in the current round, but they all fail to consider the impact of future demands and suppliers on the current dispatching decision. This could lead to taking a suboptimal dispatching decision from the future perspective. To solve this problem, we propose a novel demand dispatching model using deep reinforcement learning. In this model, we make each demand as an agent. The action of each agent, i.e., the dispatching decision of each demand, is determined by a centralized algorithm in a coordinated way. The model works in the following two steps. (1) It learns the demand’s expected value in each spatiotemporal state using historical transition data. (2) Based on the learned values, it conducts a Many-To-Many dispatching using a combinatorial optimization algorithm by considering both immediate rewards and expected values of demands in the next round. In order to get a higher total reward, the demands with a high expected value (short response time) in the future may be delayed to the next round. On the contrary, the demands with a low expected value (long response time) in the future would be dispatched immediately. Through extensive experiments using real-world datasets, we show that the proposed model outperforms the existing models in terms of Cancellation Rate and Average Response Time.

Comprehensive Income: which potential effects on firms' performance evaluation and users' decision process?

2016 ◽  
pp. 55-94
Author(s):  
Pier Luigi Marchini ◽  
Carlotta D'Este
Keyword(s):  
Performance Evaluation ◽  
Decision Process ◽  
Financial Analysts ◽  
Listed Companies ◽  
Financial Statement ◽  
Comprehensive Income ◽  
Other Comprehensive Income ◽  
Gains And Losses ◽  
The Impact

The reporting of comprehensive income is becoming increasingly important. After the introduction of Other Comprehensive Income (OCI) reporting, as required by the 2007 IAS 1-revised, the IASB is currently seeking inputs from investors on the usefulness of unrealized gains and losses and on the role of comprehensive income. This circumstance is of particular relevance in code law countries, as local pre-IFRS accounting models influence financial statement preparers and users. This study aims at investigating the role played by unrealized gains and losses reporting on users' decision process, by examining the impact of OCI on the Italian listed companies RoE ratio and by surveying a sample of financial analysts, also content analysing their formal reports. The results show that the reporting of comprehensive income does not affect the financial statement users' decision process, although it statistically affects Italian listed entities' performance.

ANALISIS RISIKO OPERASIONAL MENGGUNAKAN PENDEKATAN DISTRIBUSI KERUGIAN DENGAN METODE AGREGAT

2011 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Y. ARBI ◽  
R. BUDIARTI ◽  
I G. P. PURNABA
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Financial Institution ◽  
Expected Value ◽  
Insurance Companies ◽  
Limited Data ◽  
Loss Distribution Approach ◽  
Potential Loss ◽  
The Impact

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes or external problems. Insurance companies as financial institution that also faced at risk. Recording of operating losses in insurance companies, were not properly conducted so that the impact on the limited data for operational losses. In this work, the data of operational loss observed from the payment of the claim. In general, the number of insurance claims can be modelled using the Poisson distribution, where the expected value of the claims is similar with variance, while the negative binomial distribution, the expected value was bound to be less than the variance.Analysis tools are used in the measurement of the potential loss is the loss distribution approach with the aggregate method. In the aggregate method, loss data grouped in a frequency distribution and severity distribution. After doing 10.000 times simulation are resulted total loss of claim value, which is total from individual claim every simulation. Then from the result was set the value of potential loss (OpVar) at a certain level confidence.

High dietary restraint improves performance on a food-motivated probabilistic selection task

2019 ◽  
Author(s):  
Jennifer R Sadler ◽  
Grace Elisabeth Shearrer ◽  
Nichollette Acosta ◽  
Kyle Stanley Burger
Keyword(s):  
Reinforcement Learning ◽  
Instrumental Conditioning ◽  
Food Reinforcement ◽  
Dietary Restraint ◽  
Memory Task ◽  
Brain Response ◽  
Anthropometric Measures ◽  
Instrumental Task ◽  
The Impact ◽  
Reward And Punishment

BACKGROUND: Dietary restraint represents an individual’s intent to limit their food intake and has been associated with impaired passive food reinforcement learning. However, the impact of dietary restraint on an active, response dependent learning is poorly understood. In this study, we tested the relationship between dietary restraint and food reinforcement learning using an active, instrumental conditioning task. METHODS: A sample of ninety adults completed a response-dependent instrumental conditioning task with reward and punishment using sweet and bitter tastes. Brain response via functional MRI was measured during the task. Participants also completed anthropometric measures, reward/motivation related questionnaires, and a working memory task. Dietary restraint was assessed via the Dutch Restrained Eating Scale. RESULTS: Two groups were selected from the sample: high restraint (n=29, score >2.5) and low restraint (n=30; score <1.85). High restraint was associated with significantly higher BMI (p=0.003) and lower N-back accuracy (p=0.045). The high restraint group also was marginally better at the instrumental conditioning task (p=0.066, r=0.37). High restraint was also associated with significantly greater brain response in the intracalcarine cortex (MNI: 15, -69, 12; k=35, pfwe< 0.05) to bitter taste, compared to neutral taste.CONCLUSIONS: High restraint was associated with improved performance on an instrumental task testing how individuals learn from reward and punishment. This may be mediated by greater brain response in the primary visual cortex, which has been associated with mental representation. Results suggest that dietary restraint does not impair response-dependent reinforcement learning.

scholarly journals An Evaluation Methodology for Interactive Reinforcement Learning with Simulated Users

Biomimetics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Adam Bignold ◽  
Francisco Cruz ◽  
Richard Dazeley ◽  
Peter Vamplew ◽  
Cameron Foale
Keyword(s):  
Reinforcement Learning ◽  
Information Source ◽  
Human Interaction ◽  
Evaluation Methodology ◽  
External Information ◽  
Preliminary Evaluation ◽  
Learning Agents ◽  
Learning Agent ◽  
Knowledge Bias ◽  
The Impact

Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent.

scholarly journals Mandatory annuitization and money's worth: evidence from Singapore

2021 ◽  
pp. 1-20
Author(s):  
Joelle H. Fong ◽  
Jackie Li
Keyword(s):  
Expected Value ◽  
Longevity Risk ◽  
Future Trends ◽  
The Past ◽  
The Future ◽  
Population Mortality ◽  
Mortality Improvement ◽  
Life Annuities ◽  
Compulsory Purchase ◽  
The Impact

Abstract This paper examines the impact of uncertainties in the future trends of mortality on annuity values in Singapore's compulsory purchase market. We document persistent population mortality improvement trends over the past few decades, which underscores the importance of longevity risk in this market. Using the money's worth framework, we find that the life annuities delivered expected payouts valued at 1.019–1.185 (0.973–1.170) per dollar of annuity premium for males (females). Even in a low mortality improvement scenario, the annuities provide an expected value exceeding 0.950. This suggests that participants in the national annuity pool have access to attractively priced annuities, regardless of sex, product, and premium invested.

scholarly journals Evaluating polymer interplay after hot water pretreatment to investigate maize stem internode recalcitrance

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Amandine Leroy ◽  
Xavier Falourd ◽  
Loïc Foucat ◽  
Valérie Méchin ◽  
Fabienne Guillon ◽  
...  
Keyword(s):  
Cell Wall ◽  
Negative Impact ◽  
Lignin Content ◽  
Structural Features ◽  
Hot Water ◽  
Condensed State ◽  
Structural Factors ◽  
Water Pretreatment ◽  
Hot Water Pretreatment ◽  
The Impact

Abstract Background Biomass recalcitrance is governed by various molecular and structural factors but the interplay between these multiscale factors remains unclear. In this study, hot water pretreatment (HWP) was applied to maize stem internodes to highlight the impact of the ultrastructure of the polymers and their interactions on the accessibility and recalcitrance of the lignocellulosic biomass. The impact of HWP was analysed at different scales, from the polymer ultrastructure or water mobility to the cell wall organisation by combining complementary compositional, spectral and NMR analyses. Results HWP increased the kinetics and yield of saccharification. Chemical characterisation showed that HWP altered cell wall composition with a loss of hemicelluloses (up to 45% in the 40-min HWP) and of ferulic acid cross-linking associated with lignin enrichment. The lignin structure was also altered (up to 35% reduction in β–O–4 bonds), associated with slight depolymerisation/repolymerisation depending on the length of treatment. The increase in $${T}_{1\rho }^{H}$$ T 1 ρ H , $${T}_{HH}$$ T HH and specific surface area (SSA) showed that the cellulose environment was looser after pretreatment. These changes were linked to the increased accessibility of more constrained water to the cellulose in the 5–15 nm pore size range. Conclusion The loss of hemicelluloses and changes in polymer structural features caused by HWP led to reorganisation of the lignocellulose matrix. These modifications increased the SSA and redistributed the water thereby increasing the accessibility of cellulases and enhancing hydrolysis. Interestingly, lignin content did not have a negative impact on enzymatic hydrolysis but a higher lignin condensed state appeared to promote saccharification. The environment and organisation of lignin is thus more important than its concentration in explaining cellulose accessibility. Elucidating the interactions between polymers is the key to understanding LB recalcitrance and to identifying the best severity conditions to optimise HWP in sustainable biorefineries.

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