Suboptimal Choices Research Articles

An instantly applicable machine learning model for extraprostatic extension prediction in prostate cancer: A post-hoc analysis of the RAPID 68Ga-PSMA PET/MRI trial

Radical prostatectomy (RP) is a common first-line treatment for patients with localized prostate cancer (PCa), with nerve-sparing techniques recommended to minimize adverse effects on quality of life. Accurate pre-operative detection of extraprostatic extension (EPE) remains difficult, often resulting in suboptimal treatment choices. Machine learning (ML) has the potential to improve pre-operative EPE detection through the integration of multimodal data, but its clinical adoption is hindered by implementation complexities and explainability issues. This study conducted a post-hoc analysis of a prospective clinical trial (NCT02659527) involving patients who underwent [68Ga]Ga-PSMA-11 PET/MRI from 2014 to 2019. Only the clinically used parameters were collected, including PET-derived features, blood-based markers, histology-derived parameters, and patient demographics. ML models were developed using either only non-invasive features or a combination of non-invasive and invasive features for EPE detection and were compared to conventional PET image readings. Among the 77 patients included in the study, 44 (57%) had EPE based on post-surgical findings. The ML models outperformed traditional PET image readings in diagnostic accuracy, with the explainable boosting machine (EBM) model achieving an AUC of 0.88. The ML model incorporating invasive features showed superior predictive ability for EPE compared to visual clinical assessments (AUC 0.88 vs. 0.71, p 0.02), while the difference between the ML model with non-invasive features and clinical assessments was not significant (AUC 0.83 vs. 0.71, p 0.34). Explainable ML models utilizing routinely acquired clinical data can greatly enhance the pre-operative detection of EPE in PCa patients, potentially leading to more accurate clinical staging, better treatment decisions, and improved patient outcomes. Please click on the 'PDF' for the full abstract!

High‐Latitude Eddy Covariance Temporal Network Design and Optimization

AbstractEcosystems at high latitudes are changing rapidly in response to climate change. To understand changes in carbon fluxes across seasonal to multi‐decadal timescales, long‐term in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high‐latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning‐based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter‐term measurements functions better in terms of larger‐scale representativeness than a network with fewer long‐term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. For the Canadian Arctic we show several optimization scenarios and find that especially the Canadian high Arctic and north east tundra benefit greatly from addition sites. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.

Optimal Pediatric Outpatient Antibiotic Prescribing

In the US, 50% of all pediatric outpatient antibiotics prescribed are unnecessary or inappropriate. Less is known about the appropriateness of pediatric outpatient antibiotic prescribing. To identify the overall percentage of outpatient antibiotic prescriptions that are optimal according to guideline recommendations for first-line antibiotic choice and duration. This cross-sectional study obtained data on any clinical encounter for a patient younger than 20 years with at least 1 outpatient oral antibiotic, intramuscular ceftriaxone, or penicillin prescription filled in the state of Tennessee from January 1 to December 31, 2022, from IQVIA's Longitudinal Prescription Claims and Medical Claims databases. Each clinical encounter was assigned a single diagnosis corresponding to the lowest applicable tier in a 3-tier antibiotic tier system. Antibiotics prescribed for tier 1 (nearly always required) or tier 2 (sometimes required) diagnoses were compared with published national guidelines. Antibiotics prescribed for tier 3 (rarely ever required) diagnoses were considered to be suboptimal for both choice and duration. Primary outcome was the percentage of optimal antibiotic prescriptions consistent with guideline recommendations for first-line antibiotic choice and duration. Secondary outcomes were the associations of optimal prescribing by diagnosis, suboptimal antibiotic choice, and patient- and clinician-level factors (ie, age and Social Vulnerability Index) with optimal antibiotic choice, which were measured by odds ratios (ORs) and 95% CIs calculated using a multivariable logistic regression model. A total of 506 633 antibiotics were prescribed in 488 818 clinical encounters (for 247 843 females [50.7%]; mean [SD] age, 8.36 [5.5] years). Of these antibiotics, 21 055 (4.2%) were for tier 1 diagnoses, 288 044 (56.9%) for tier 2 diagnoses, and 197 660 (39.0%) for tier 3 diagnoses. Additionally, 194 906 antibiotics (38.5%) were optimal for antibiotic choice, 259 786 (51.3%) for duration, and 159 050 (31.4%) for both choice and duration. Acute otitis media (AOM) and pharyngitis were the most common indications, with 85 635 of 127 312 (67.3%) clinical encounters for AOM and 42 969 of 76 865 (55.9%) clinical encounters for pharyngitis being optimal for antibiotic choice. Only 257 of 4472 (5.7%) antibiotics prescribed for community-acquired pneumonia had a 5-day duration. Optimal antibiotic choice was more likely in patients who were younger (OR, 0.98; 95% CI, 0.98-0.98) and were less socially vulnerable (OR, 0.84; 95% CI, 0.82-0.86). This cross-sectional study found that less than one-third of antibiotics prescribed to pediatric outpatients in Tennessee were optimal for choice and duration. Four stewardship interventions may be targeted: (1) reduce the number of prescriptions for tier 3 diagnoses, (2) increase optimal prescribing for AOM and pharyngitis, (3) provide clinician education on shorter antibiotic treatment courses for community-acquired pneumonia, and (4) promote optimal antibiotic prescribing in resource-limited settings.

Weighing up the options: experiences in applying decision science from a large-scale conservation program

ABSTRACT The need to make evidence-based decisions in conservation planning for threatened species in the face of limited resources and knowledge is widely recognised as a growing challenge. Increasingly sophisticated decision-support tools and approaches are available to conservation programs. The ability of conservation planners to effectively implement these tools will be key to incorporating complex information into threatened species management. The development of effective decision science approaches does not end when they are made available to planners. Planner and practitioner input into their use and outputs is an important part of incorporating these tools into on-ground conservation. The New South Wales Saving our Species program is a large-scale conservation program with jurisdiction over more than 1100 threatened species, ecological communities and populations. We discuss why co-design is key to successful implementation of decision science in program-level planning; this approach has supported the Saving our Species program to account for forms of knowledge that may otherwise be ignored by data driven optimisation. This paper focuses on the role of conservation planners in developing and applying decision tools. We present three case studies that deployed tools co-developed for the Saving our Species program. Through these case studies, we suggest that effective conservation planning can be best achieved through (1) narrowing down the number of options under consideration, by eliminating sub-optimal choices (2) supporting decision-makers to understand the relative advantages and disadvantages of the choices under consideration and (3) enhancing the effectiveness of decision-support tools by integrating practitioner expertise into their application.

Goodness-of-fit tests based on Kullback-Leibler divergence for bladder cancer survival analysis: Applications to exponentiated exponential distribution

Bladder cancer is among the ten most common types of cancer worldwide, with approximately 550,000 new cases occurring each year. It accounts for comprehensively compared to 3% of all newly diagnosed cancer cases and contributes to 2.1% of cancer-related deaths globally. This article introduces goodness-of-fit tests that aim to fit the exponentialized exponential distribution. These tests are based on the Kullback-Leibler difference and have been applied to censored and complete samples of Bladder Cancer Patients. We calculated critical values and statistical power measurements, considering the best and worst bandwidth scenarios. We then comprehensively compared essential values and power across various parameters, accounting for optimal and suboptimal bandwidth choices derived from the Kullback–Leibler difference. In the final phase of our study, we used a dataset of individuals diagnosed with bladder cancer to demonstrate the practical applicability of our proposed research. Finally, this modeling type can benefit researchers and healthcare professionals through time-to-event analysis (survival analysis), investigation of events, medical decision-making, and risk prediction.

Care Planning in Ageing Families during the COVID-19 Pandemic and Social Unrest in Hong Kong

AbstractThe role of care planning in addressing eldercare challenges has received increasing attention. However, the influence of the uncertainty triggered by the coronavirus disease 2019 (COVID-19) pandemic, in conjuction with significant social unrest, on family eldercare planning remains unclear. This study examines eldercare planning experiences of families in Hong Kong, focusing on the evolving changes and adaptations faced by both local residents and mainland Chinese immigrants during the COVID-19 pandemic and the 2019–2020 social unrest. Through purposive sampling, we conducted semi-structured interviews with 93 local residents and 66 migrants from 2020 to 2021. Five main themes emerged: (1) increased awareness of healthcare preparation and planning during the COVID-19 pandemic, (2) residential care homes as a suboptimal choice after the COVID-19 pandemic, (3) influence of the COVID-19 pandemic on financial planning for eldercare, (4) distant family care after the social unrest, and (5) the need to improve remote care under border restrictions. This study contributes to the literature by examining the intersection of crisis events and eldercare planning, viewing the family as a planning unit. It also deepens the understanding of caregiving within the context of health and social crises, providing valuable insights for the development of future support systems for older adults and their caregivers in similar circumstances.

Impact of Psychological Factors on Investment Decisions in Nepalese Share Market: A Mediating Role of Financial Literacy

The Nepalese stock market is characterized by emerging market dynamics and notable volatility. It is influenced by factors such as regulatory developments, economic conditions, and investor sentiment, shaping its growth trajectory and investor behavior. This study examines the influence of psychological factors on investment decisions within the Nepalese share market, emphasizing the mediating role of financial literacy. To achieve the stated objective, data were collected from the primary source using a structured questionnaire administered through random sampling technique. The respondents comprised 410 individual investors inside the Kathmandu Valley. The SmartPLS 4.0 was used to analyze the structural relationships within the proposed theoretical model. The findings of this study validated the set hypotheses that psychological factors have positive and significant effect on investment decisions among the investors in Nepalese share market. In the context of Nepal, where the share market is characterized by volatility and emerging market dynamics, these psychological biases often lead to suboptimal investment choices. Findings indicate that investors with higher financial literacy levels are better equipped to mitigate the adverse effects of psychological biases, leading to more rational and informed investment decisions. The study highlights the critical need for enhanced financial education programs to empower investors and improve overall market efficiency. By understanding the interplay between psychological factors and financial literacy, policymakers and financial institutions can develop targeted strategies to foster a more stable and resilient investment environment in Nepal.

Enterprise rent‐seeking and high‐quality development: A perspective based on institutional deficiencies

AbstractBased on the puzzle of mismatch between China's current innovation environment construction and firms' innovation achievements, this paper uses data of Chinese A‐share listed firms and tries to explore whether rent‐seeking is a strategic choice for firms to achieve high‐quality development in the context of institutional deficiencies. This paper finds that (1) corporate rent‐seeking can significantly increase total factor productivity (TFP), a result that still holds after various robustness tests such as the instrumental variables method. The mechanism test finds that corporate rent‐seeking can significantly incentivize corporate innovation, especially high‐quality innovation. (2) Rent‐seeking has a stronger effect on TFP in high‐tech enterprises, patent‐intensive industries, and non‐overcapacity industries; institutional factors such as the reasonableness of the government‐market relationship and the degree of distortion in the factor market have a significant negative and positive moderating effect on the promotion of TFP by rent‐seeking, respectively. Further sub‐sample tests show that the TFP promotion effect of rent‐seeking in firms with stronger willingness and demand for innovation is more influenced by the institutional environment factors. The above results suggest that rent‐seeking can act as an alternative means for firms to satisfy their innovation needs in the absence of institutions, and thus increase TFP. (3) Industry‐based tests show that rent‐seeking can promote overall technological progress, but it does not have the “lubricant” effect of optimizing the allocation of resources, and rent‐seeking is always a suboptimal choice for the allocation of resources. Unlike previous studies that emphasized the negative consequences of rent‐seeking, we highlight the potential benefits of rent‐seeking in the context of China's transition economy from a total factor productivity (TFP) perspective, which enriches the research in the area of rent‐seeking and TFP.

Recurring suboptimal choices result in superior decision making.

Recurring suboptimal choices result in superior decision making.

Supplemental Material for Recurring Suboptimal Choices Result in Superior Decision Making

Supplemental Material for Recurring Suboptimal Choices Result in Superior Decision Making

Noradrenergic modulation of stress induced catecholamine release: Opposing influence of FG7142 and yohimbine.

Life stress modulates decision making, particularly in the face of risk, in some cases prompting vulnerable populations to make suboptimal, life-altering choices. In the brain, stress is known to alter the extracellular release of catecholamines in structures such as basolateral amygdala (BLA) and nucleus accumbens (NAc), but the relationship between catecholamines and decision-making behavior under stress has not been systemically explored. We developed an operant touchscreen decision-making task for rats comprising elements of loss aversion and risk seeking behavior. Rats were first injected systemically with an adrenergic -receptor agonist (guanfacine) and antagonist (yohimbine), as well as a partial inverse GABAA agonist, FG 7142, known to induce anxiety and stress related physiological responses in a variety of species, including humans. We then used fiber photometry to monitor NE in the basolateral amygdala (BLA), and DA activity in the nucleus accumbens (NAc) while animals engaged in decision-making and following systemic injections of FG 7142 and yohimbine. Neither yohimbine nor guanfacine had any impact on decision making strategy but altered motivational state with yohimbine making the animal almost insensitive to the reward outcome. The pharmacological induction of stress with FG 7142 biased the rats' decisions towards safety, but this bias shifted toward risk when co-treated with yohimbine. In the BLA and NAc, the FG 7142 altered catecholamine release, with systemic yohimbine producing opposing effects on NE and DA release. Stress induced changes in catecholamine release in the BLA and NAc can directly influence loss sensitivity, decisions and motivation, which can be modulated by the adrenoreceptor antagonist, yohimbine.

SDN-based reliable emergency message routing schema using Digital Twins for adjusting beacon transmission in VANET

Digital Twin (DT) has revolutionized the contextualized digital environment. This advancement enables real-time monitoring and simulation of events, leading to more effective decision-making. In smart transportation, DT plays a crucial role in enhancing various aspects of road decision-making, including optimizing routing decisions for Emergency Message (EM) forwarding in Vehicular Ad hoc Networks (VANETs). In this study, DT has integrated with cutting-edge technologies such as Software-Defined-Network (SDN) and geographically distributed computing (fog computing) to elevate the position-based routing behavior in two directions: selecting the optimal next-hop and taking control of periodic messages (beacon transmission). The nature of routing decisions can be complex due to constant movement and frequent position changes of vehicles in the VANET system. Merely selecting the next-hop vehicle without considering its stability and predictability can result in suboptimal routing choices. To this end, SDN based on Reliable Emergency Message Routing Schema (SDN-REMR) is introduced. This schema utilizes DT and aims to leverage the behaviors of moving vehicles to enhance the delivery of EM by providing a reliable routing mechanism. To eliminate unstable neighbors from the list of potential next-hops, SDN-REMR forecasts the relative positions of surrounding vehicles using Euclidean distance and position information. Moreover, SDN-REMR uses the movement information collected from vehicles (such as position, speed, and power consumption) to reduce the possibility of a link disruption and select the best next-hop for reliable communication. SDN-REMR enables the Store-Carry-Forward (SCF) approach to ensure packets are successfully forwarded to the destination, even in challenging network conditions. This control strategy aims to strike a balance between maintaining reliable communication and minimizing congestion in the network. The experimental results prove that there are 42.6%, 76.22%, 76.22%, and 22.2% enhancement in terms of network throughput, total delivered packets, number of dropped packets, and routing overhead.

Dancing with Uncertainty: Unraveling Firm Investment Inefficiencies in the Asia Pacific Region

This study explores the intricate relationship between uncertainty and corporate investment inefficiencies in the Asia-Pacific region, utilizing data from non-financial firms between 2008 and 2021. The method used in the study is fixed effect regression with Driscoll-Kraay robust standard error. The empirical analysis unveils that uncertainty leads to overinvestment. This phenomenon is more pronounced in middle and low-income economies, while high-income countries display a distinct trend of less susceptibility to uncertainty-induced suboptimal investment choices. The study’s implications extend to policymakers and industry stakeholders, urging a closer examination of firms’ risk management strategies, particularly considering the strategic potential of overinvestment as a buffer against uncertainty’s adverse effects. This holds particular significance in the dynamic economic landscape of the Asia-Pacific countries, where the study contributes to a deeper understanding of the interplay between uncertainty and inefficiency of investment decisions across diverse economic settings.

Subthalamic control of impulsive actions: insights from deep brain stimulation in Parkinson's disease.

Control of actions allows adaptive, goal-directed behaviour. The basal ganglia, including the subthalamic nucleus, are thought to play a central role in dynamically controlling actions through recurrent negative feedback loops with the cerebral cortex. Here, we summarize recent translational studies that used deep brain stimulation to record neural activity from and apply electrical stimulation to the subthalamic nucleus in people with Parkinson's disease. These studies have elucidated spatial, spectral and temporal features of the neural mechanisms underlying the controlled delay of actions in cortico-subthalamic networks and demonstrated their causal effects on behaviour in distinct processing windows. While these mechanisms have been conceptualized as control signals for suppressing impulsive response tendencies in conflict tasks and as decision threshold adjustments in value-based and perceptual decisions, we propose a common framework linking decision-making, cognition and movement. Within this framework subthalamic deep brain stimulation can lead to suboptimal choices by reducing the time that patients take for deliberation before committing to an action. However, clinical studies have consistently shown that the occurrence of impulse control disorders is reduced, not increased, after subthalamic deep brain stimulation surgery. This apparent contradiction can be reconciled when recognizing the multifaceted nature of impulsivity, its underlying mechanisms and modulation by treatment. While subthalamic deep brain stimulation renders patients susceptible to making decisions without proper forethought, this can be disentangled from effects related to dopamine comprising sensitivity to benefits vs. costs, reward delay aversion and learning from outcomes. Alterations in these dopamine-mediated mechanisms are thought to underlie the development of impulse control disorders, and can be relatively spared with reduced dopaminergic medication after subthalamic deep brain stimulation. Together, results from studies using deep brain stimulation as an experimental tool have improved our understanding of action control in the human brain and have important implications for treatment of patients with Neurological disorders.

Heuristic biases influencing individual stock investment decisions: a hybrid fuzzy DELPHI-AHP-DEMATEL approach

PurposeStock investing choices of individual investors are predominantly influenced by heuristic biases, leading to sub-optimal choices. Accordingly, this study aims to identify, categorize, validate, prioritize, and find causality among the heuristic biases shaping stock investment decisions of individual investors.Design/methodology/approachThis research offers original contribution by employing a hybrid approach combining fuzzy DELPHI method (FDM), fuzzy analytical hierarchy process (FAHP), and fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) techniques to validate, prioritize, and find causality among the heuristic biases.FindingsTwenty sub-heuristic biases were identified under five main heuristic bias categories. Out of which, 17 were validated using FDM. Further, availability and representativeness within main heuristic categories, and availability cascade and retrievability within sub-heuristic biases were prioritized using FAHP. Overconfidence and availability were identified as the causes among the five main biases by F-DEMATEL.Practical implicationsThis study offers the stock investors a deeper understanding of heuristic biases and empowers them to make rational investment decisions.Originality/valueThis paper is the inaugural effort to identify, categorize, validate, prioritize and examine the cause-and-effect relationship among the heuristic biases.

DELEGATION OF AUTHORITY AND EMPLOYEE PERFORMANCE AFFECT MANAGEMENT DECISION MAKING EFFECTIVENESS IN PT. SOLUSI GUNA SEJAHTERA-BEKASI

This study explores the relationship between effective Delegation of authority and employee performance, highlighting the importance of clear communication, trust-building, and well-defined responsibilities in managerial decision-making. It examines the role of Delegation as a strategic tool for managers to empower employees and the impact of employee performance on management decision-making. High-performing employees can contribute valuable insights, innovative ideas, and data-based recommendations, while poor performance can lead to errors, misjudgments, and suboptimal choices. The interaction between Delegation and employee performance is analyzed, revealing a symbiotic relationship. Effective Delegation can stimulate employee engagement and motivation, leading to improved performance, while outstanding performance can strengthen managers' confidence in delegations, allowing them to entrust more responsibility to employees. On a scale of 1 to 5, the statements in the questionnaire are rated. SEM (Structural Equal Model) analysis on SmartPLS 4.0 in use the research findings indicate that (1) employee performance positively influences managerial decision-making, and (2) delegation positively influences managerial decision-making.

Human Behavior in Suboptimal Choice Tasks: Defining Optimality.

Suboptimal choice behavior, or behavior that leads to a loss of resources over time, has been observed in a laboratory setting from multiple species. A procedure commonly used to capture this effect involves presenting two alternatives during choice trials, one of which is optimal whereas the other is suboptimal. The optimal alternative yields reinforcement more often than the suboptimal alternative, but often does not produce signals that indicate whether reinforcement will occur. The suboptimal alternative produces less reinforcement than the optimal alternative but may include reinforcement-predictive stimuli that indicate to the organism whether reinforcement will occur. This procedural framework has consistently produced a preference for the suboptimal alternative in pigeons and, to a lesser extent, rats. However, human participants have demonstrated preference for the optimal alternative. Following a review of past suboptimal choice research, we applied the reinforcement-derived definition of optimality to two sets of our previously published human data. We found that under multiple conditions, human choice behavior was consistent with what was predicted by the proportion of obtained reinforcement, thus supporting that the behavior was optimal. However, we found that participants in two conditions chose the suboptimal alternative more than expected. This finding could be considered as a demonstration of suboptimal choice in humans. We propose that comparing choice behavior to what past obtained reinforcement outcomes would predict might be a more accurate view of whether patterns of choice are within the parameters of this task.

A painting authentication method based on multi-scale spatial-spectral feature fusion and convolutional neural network

The scientific authentication of paintings holds significant importance within the realm of art collection. Employing convolutional neural networks for the classification of authentic and counterfeit painting images based on color images is a viable but suboptimal choice. This study investigates the potential for authenticating paintings by incorporating high-spectral images alongside high-resolution spatial images. High-resolution and high-spectral images of genuine and counterfeit paintings were acquired using a push-broom digital scanning system. The processing methods presented in this approach for the acquired images are: 1) The study utilized the circular local binary pattern (LBP) and principal component analysis (PCA) to extract surface texture and spectral data from Chinese character images in paintings, encompassing both spatial and spectral dimensions. 2) The technique utilizing non-subsampling Shearlet transform (NSST) and pulse-coupled neural network (PCNN) was employed to integrate the spatial and spectral characteristics of the images into a pseudo-color image, producing a dataset of feature data for genuine and counterfeit paintings. 3) The experiments aimed to achieve the authenticity of artworks using EfficientNet v2-s, the hyperparameters of which were fine-tuned accordingly. The experimental findings demonstrate that this approach attained a 90.8 % accuracy on the test dataset, representing a 3.5 % enhancement over the existing top-performing three-dimensional convolutional neural network (3D-CNN).

The Impact of Rural–Urban Student Mobility on the Efficiency of Resource Allocation in China’s Rural Households: Optimization or Distortion?

The impact of rural-urban student mobility on the efficiency of resource allocation in China’s rural households is crucial for refining policies related to rural migrant settlement and the balanced allocation of urban and rural educational resources. Drawing on theoretical insights, this study employs a mixed methods approach—primarily qualitative, with quantitative support—to empirically test the impact of rural-urban student mobility on the resource allocation efficiency of rural households in China. Utilizing micro-survey data from China Family Panel Studies (CFPS), this study applies ordinary least squares (OLS) models, propensity score matching difference-in-differences (PSM-DID) models, and endogenous switching regression (ESR) models to ascertain the direction of this impact. Furthermore, this study incorporates in-depth interview data gathered from field research in D County to explore the dynamic mechanisms of resource allocation adjustments within these households. The results show that rural-urban student mobility significantly reduces the efficiency of labor resource allocation and agricultural production in China’s rural households. The impact is heterogeneous across different groups, most negatively affecting households with limited labor resources, lower educational levels, lower incomes, more productive fixed assets, and less self-owned land. Mobility leads to suboptimal occupational choices, resulting in decreased labor resource efficiency. Additionally, it distorts agricultural production by leading to labor loss, reduced investments in agricultural productivity, and misaligned land allocation decisions.

Temporal context effects on suboptimal choice.

Choice can be driven both by rewards and stimuli that signal those rewards. Under certain conditions, pigeons will prefer options that lead to less probable reward when the reward is signaled. A recently quantified model, the Signal for Good News (SiGN) model, assumes that in the context of uncertainty, signals for a reduced delay to reward reinforce choice. The SiGN model provides an excellent fit to previous results from pigeons and the current studies are the first to test a priori quantitative predictions. Pigeons chose between a suboptimal alternative that led to signaled 20% food and an optimal alternative that led to 50% food. The duration of the choice period was manipulated across conditions in two experiments. Pigeons strongly preferred the suboptimal alternative at the shorter durations and strongly preferred the optimal alternative at the longer durations. The results from both experiments fit well with predictions from the SiGN model and show that altering the duration of the choice period has a dramatic effect in that it changes which of the two options pigeons prefer. More generally, these results suggest that the relative value of options is not fixed, but instead depends on the temporal context.