Choice Probabilities Research Articles

Personalized Pricing and Assortment Optimization Under Consumer Choice Models with Local Network Effects

We introduce a consumer choice model where each consumer’s utility is affected by their neighbors’ purchase probabilities in a network. We first characterize the choice probabilities and then consider the associated personalized assortment optimization problem. Although this problem is NP-hard, we show that for star networks, the optimal assortment to the central consumer cannot be strictly larger than that without network effects; and the optimal assortment to each peripheral consumer must be a revenue-ordered assortment. Then, we propose a novel idea in which the sellers are allowed to offer “randomized assortments” to each node in the network by viewing each node as a consumer group. We show that randomized assortments may further increase the revenue, and for general network setting, under certain conditions, the optimal assortment must be a combination of two adjacent revenue-ordered assortments. Finally, we study the associated optimal pricing problem and develop structural properties and efficient algorithms.

Random Forest estimation of the ordered choice model

AbstractIn this paper we develop a new machine learning estimator for ordered choice models based on the Random Forest. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with nonlinearities and high correlation among covariates. An empirical application contrasts the estimation of marginal effects and their standard errors with an Ordered Logit model. A software implementation of the Ordered Forest is provided both in and in the package available on and , respectively.

Growing Attention

We model a boundedly rational agent who exhibits limited and heterogeneous attention. The agents' attention can be naturally sorted in an ascending order. We provide a characterization result using two simple behavioral postulates. Unlike earlier models, consideration sets' composition and frequency are uniquely identified. Within our framework, we accommodate unobserved heterogeneity in attention and unobserved preferences. Furthermore, we explore various extensions of our model and provide insights into the comparative statics of attentiveness levels in two probabilistic choices.

Assessment of malaria transmission in Kenya using multilevel logistic regression

Background: Kenya has a lower malaria incidence in comparison to other African malaria-endemic nations. Malaria is a significant public health concern in the country. The malaria indicator survey (MIS) data were analyzed using the logistic regression model. Nonetheless, independent data may be the cause of most MIS's hierarchical structure. This approach does not consider any association between data points within a cluster, as it assumes that the individual malaria statuses are independent of their causes. The approach may lead to biased analysis conclusions. The primary goal of this research is to determine the impact of sample enumeration areas (SEAs) and SEA features on individual malaria rapid diagnostic test (RDT) results. We are interested in identifying key factors influencing household members' malaria RDT findings or Kenya's malaria prevalence and assessing variation. Methods: Our study utilized the robust 2020 Kenya National Malaria Indicator Surveys (KMIS) dataset, which is representative of the entire nation. This dataset, comprising 301 clusters (134 urban and 167 rural areas), was instrumental in applying several multilevel models, including random sample and sample Enumeration Area (SEA) effects. We also considered the weights used in the s survey design, which is used to adjust uneven probabilities of choice within clusters, further enhancing the reliability and relevance of our findings. The methods used in this study involved a rigorous analysis of the KMIS dataset, including applying multilevel models and considering survey design weights to ensure the robustness and strength of our results. ResultsThis study's findings are significant and crucial in understanding the prevalence of malaria in Kenya. The findings reveal that factors such as region, place of residence, mosquito bed net use, water source location, wealth index, age, household size, and altitude are significantly associated with malaria's prevalence.After accounting for these variables, systematic changes across SEAs accounted for approximately 47.1% of the remaining variability in malaria occurrence in the study locations. In contrast, the remaining 52.9% was projected to be unmeasured differences between individuals or family units. These findings provide a detailed explanation of the various processes that influence malaria prevalence in Kenya. Conclusions: The study's multilevel logistic regression model, which includes random effects, identified two SEA-level and eight individual/household risk factors for malaria infection. Thus, increasing the availability of insecticide-treated bed nets is one crucial element that public health policymakers should consider. Furthermore, health planners can organize spatially targeted initiatives to prevent malaria transmission with the help of spatial clustering data.

Somatosensory stimulation on the wrist enhances the subsequent hand-choice by biasing toward the stimulated hand.

Hand choice is an unconscious decision frequently made in daily life. The electroencephalogram before target presentation correlates with hand choice for the target where hand choice probability reaches equilibrium. However, whether neurophysiological interventions before target presentation influence hand choice remains unknown. Therefore, this study determined whether instantaneous somatosensory electrical stimulation administered to the unilateral wrist at 0, 300, or 600ms before the target presentation facilitates or inhibits stimulated hand choice for targets around the hand selection equilibrium point. A single electrical stimulation comprised five trains of 1ms electrical pulses, with a 20ms inter-pulse interval. The stimulus intensity was set at 80% of the motor threshold. This study included 14 right-handed healthy adults (five females and nine males; mean age, 25.1 ± 4.64years). Unilateral wrist stimulation significantly increased the probability of choosing the stimulated hand and led to a faster reaction time than bilateral wrist stimulation and no-stimulation conditions. The results suggest that prior somatosensory stimulation significantly affects the hand-choice process, effectively promoting the selection of the stimulated hand. These findings highlight the potential application of this stimulation method in stroke rehabilitation to facilitate the use of the paretic hand.

Local detouredness: A new phenomenon for modelling route choice and traffic assignment

This study introduces the novel concept of local detouredness, i.e. detours on subsections of a route, as a new phenomenon for understanding and modelling route choice. Traditionally, Stochastic User Equilibrium (SUE) traffic assignment models have been concerned with judging the attractiveness of a route by its total route cost. However, through empirical analysis we show that considering solely the global properties of a route is insufficient. We find that it is important to consider local detouredness both when determining realistic and tractable route choice sets and when determining route choice probabilities. For example, analysis of observed route choice data shows that route usage tends to decay with local detouredness, and that there is an apparent limit on the amount of local detouredness seen as acceptable. No existing models can account for this systematically and consistently, which is the motivation for the new route choice model proposed in this paper: the Bounded Choice Model with Local Detour Threshold (BCM-LDT). The BCM-LDT model incorporates the effect of local detouredness on route choice probability, and has an in-built mechanism that assigns zero probabilities to routes violating a bound on total route costs and/or a threshold on local detouredness. Thereby, the model consistently predicts which routes are used and unused. Moreover, the probability expression is closed-form and continuous. SUE conditions for the BCM-LDT are given, and solution existence is proven. Exploiting the special structure of the problem, a novel solution algorithm is proposed where flow averaging is integrated with a modified branch-and-bound method that iteratively column-generates all routes satisfying local and global bounds. Numerical experiments are conducted on small-scale and large-scale networks, establishing that equilibrated solutions can be found and demonstrating the influence of the BCM-LDT parameters on choice set size and flow allocation.

ANALISIS PROBABILITAS PEMILIHAN MODA PUBLIC BOAT DAN FAST BOAT RUTE PENYEBERANGAN BANGSAL – GILI INDAH

The most popular modes of transportation on the crossing from Bangsal Harbor to Gili Indah are public boats and fast boats. The respective advantages and disadvantages of these two modes are that fast boats excel in fast travel times, while public boats excel in cheaper travel costs. The aim of this research is to obtain factors that influence passengers' decisions in choosing a mode, to find out user characteristics and to obtain the probability that passengers choose public boats and fast boats.Data was obtained from the results of a questionnaire using the stated preference method which offers 7 change scenarios and related choices such as cost (X1), travel time (X2), and waiting time (X3) regarding the choice of public boat and fast boat modes (Y). The results of the choices of public boat and fast boat users will be known through the proportion of public interest in each scenario. Factors that influence mode choice can be identified using multiple linear regression analysis. Then a binary logit analysis of the difference is carried out to obtain the probability of mode choice. The research results show that the utility function equation is Y = -0.3864 – 0.04299X1 – 0.06177X2 – 0.02577X3. Factors that influence passengers' decisions in choosing public and fast boats are cost (X1), travel time (X2), and waiting time (X3). Using the binary logit difference method, the probability of choosing the mode for the Bangsal-Gili Indah crossing route is public boat 79% and fast boat 21%.

Economic growth versus the environment: government spending, trust, and citizen support for environmental protection

ABSTRACT Support for sustainability activities among citizens will also increase the probability of more sustainable choices being made by governments. In this study, we investigate the relationship between prioritization of environmental protection over economic growth and government spending on environmental protection in 27 OECD countries and more than 65 000 respondents. Drawing on the perspectives of the thermostatic model, complex belief systems and cognitive dissonance theory, we hypothesize that government spending and attitudes towards environmental protection are related. We further argue that this relationship will be moderated by trust in government. To investigate our hypotheses, we employ OECD statistics on government spending in combination with survey data from WVS and EVS from the years 1995 to 2019. Our findings show that increasing government spending on environmental protection is related to attitudes in favor of economic growth at the expense of environmental concerns, a relationship which is significantly strengthened by having low trust in government.

The Evolution of Virtual Concept of Education: Analysis of the Revealed Preferences and Digital Challenges

Distance education offers flexibility and accessibility to students with diverse needs and contexts. However, it is crucial to understand the factors that motivate students to choose this educational modality, especially in programs like Public Accounting, which require specific skills. The general objective of this article is to analyze the determinants in the decision to study the Public Accounting Program in a distance or virtual modality at the National Open and Distance University (UNAD). A mixed methodology is employed, supported by authors such as Creswell (2014), combining quantitative and qualitative data, and using the Probit model to identify factors influencing this choice. A significant finding is that tuition cost, the Saber 11 test score and parents' education are significant variables affecting students' decisions, with tuition cost having the greatest influence on the probability of choice.

Probabilistic performance evaluation of the class-A device in LoRaWAN protocol on the MAC layer

LoRaWAN is a network technology that provides a long-range wireless network while maintaining low energy consumption. It adopts the pure Aloha MAC protocol and the duty-cycle limitation at both uplink and downlink on the MAC layer to conserve energy. Additionally, LoRaWAN employs orthogonal parameters to mitigate collisions. However, synchronization in star-of-star topology networks and the complicated collision mechanism make it challenging to conduct a quantitative performance evaluation in LoRaWAN. Our previous work proposes a Probabilistic Timed Automata (PTA) model to represent the uplink transmission in LoRaWAN. It is a mathematical model that presents the nondeterministic and probabilistic choice with time passing. However, this model remains a work in progress. This study extends the PTA model to depict Class-A devices in the LoRaWAN protocol. The complete characteristics of LoRaWAN’s MAC layer, such as duty-cycle limits, bidirectional communication, and confirmed message transmission, are accurately modeled. Furthermore, a comprehensive collision model is integrated into the PTA. Various properties are verified using the probabilistic model checker PRISM, and quantitative properties are calculated under diverse scenarios. This quantitative analysis provides valuable insights into the performance and behavior of LoRaWAN networks under varying conditions.

Imperfect discrimination, similarity, and stochastic transitivity

We study the relationship between imperfect discrimination, similarity, and stochastic transitivity in generalized versions of Perturbed Utility (Fudenberg et al. in Econometrica 83(6):2371–2409, 2015) and Fechnerian models (Debreu in Econometrica 26(3):440–444, 1958). We show that these models are equivalent and that, within them, the properties of a similarity function can characterize all notions of stochastic transitivity (as Weak, Moderate, and Strong). Specifically, He and Natenzon (Am Econ Rev Insights 6(2):176–195, 2024) have recently shown that choice probabilities are moderately transitive if and only if the similarity function is a metric. We provide a counterpoint and show that unless choice probabilities are strongly transitive, the similarity function can violate the triangle inequality.

Clinically established early Parkinson's disease patients do not show impaired use of priors in conditions of perceptual uncertainty

The ability to use past learned experiences to guide decisions is an important component of adaptive behavior, especially when decision-making is performed under time pressure or when perceptual information is unreliable. Previous studies using visual discrimination tasks have shown that this prior-informed decision-making ability is impaired in Parkinson's disease (PD), but the mechanisms underlying this deficit and the precise impact of dopaminergic denervation within cortico-basal circuits remain unclear. To shed light on this problem, we evaluated prior-informed decision-making under various conditions of perceptual uncertainty in a sample of 13 clinically established early PD patients, and compared behavioral performance with healthy control (HC) subjects matched in age, sex and education. PD patients and HC subjects performed a random dot motion task in which they had to decide the net direction (leftward vs. rightward) of a field of moving dots and communicate their choices through manual button presses. We manipulated prior knowledge by modulating the probability of occurrence of leftward vs. rightward motion stimuli between blocks of trials, and by explicitly giving these probabilities to subjects at the beginning of each block. We further manipulated stimulus discriminability by varying the proportion of dots moving coherently in the signal direction and speed-accuracy instructions. PD patients used choice probabilities to guide perceptual decisions in both speed and accuracy conditions, and their performance did not significantly differ from that of HC subjects. An additional analysis of the data with the diffusion decision model confirmed this conclusion. These results suggest that the impaired use of priors during visual discrimination observed at more advanced stages of PD is independent of dopaminergic denervation, though additional studies with larger sample sizes are needed to more firmly establish this conclusion.

The Influence of Leadership and Organizational Culture on the Performance of Health Personnel Mediated by Work Motivation (Study of Kendari City Hospital Health Workers)

The aim of this research is to determine the influence of leadership and organizational culture on health performance performance mediated by work motivation. This research was carried out on health workers at the Kendari City Regional Hospital, with the consideration that the Kendari City Regional Hospital is one of the advanced level health service facilities belonging to the Kendari City Government which has the task of providing health services to the community. The population of this study is health workers who have the status of State Civil Apparatus, have worked for more than 2 (two) years and serve in the Kendari City Regional Hospital, totaling 267 people. To determine the sample size using the Slovin formula, at a precision level of 5%, from a total population of 267 health workers, a sample size (n) of 160 respondents was obtained. The type of inferential statistics used in this research is the t-test which is used to determine whether groups of scores have significant differences in the level of choice probability. To determine the value of the research t-test statistic, this research data was analyzed using SmartPLS software version 3.0. The results of this study found that Leadership had a positive and insignificant effect on the performance of health workers, Organizational Culture had a positive and significant effect on the Performance of health workers, Work Motivation had a positive and significant effect on the Performance of health workers, Leadership had a positive and significant effect on Work Motivation, Organizational Culture had an effect positive and significant towards Work Motivation, Work Motivation plays a role in mediating the influence of Leadership on the Performance of health workers, and Work Motivation plays a role in mediating the influence of Organizational Culture on the Performance of health workers.

Nested replicator dynamics, nested logit choice, and similarity-based learning

We consider a model of learning and evolution in games whose action sets are endowed with a partition-based similarity structure intended to capture exogenous similarities between strategies. In this model, revising agents have a higher probability of comparing their current strategy with other strategies that they deem similar, and they switch to the observed strategy with probability proportional to its payoff excess. Because of this implicit bias toward similar strategies, the resulting dynamics – which we call the nested replicator dynamics – do not satisfy any of the standard monotonicity postulates for imitative game dynamics; nonetheless, we show that they retain the main long-run rationality properties of the replicator dynamics, albeit at quantitatively different rates. We also show that the induced dynamics can be viewed as a stimulus-response model in the spirit of Erev and Roth (1998), with choice probabilities given by the nested logit choice rule of Ben-Akiva (1973) and McFadden (1978). This result generalizes an existing relation between the replicator dynamics and the exponential weights algorithm in online learning, and provides an additional layer of interpretation to our analysis and results.

Tuning cohesin trajectories enables differential readout of the Pcdhα cluster across neurons.

Expression of Protocadherin (Pcdh) genes is critical to the generation of neuron identity and wiring of the nervous system. Pcdhα genes are arranged in clusters and exhibit a range of expression profiles, from stochastic to deterministic. Because Pcdhα promoters have high sequence identity and share distal enhancers, how distinct neurons choose which gene to express remains unclear. We show that the interplay between multiple enhancers, epigenetics, and genome folding orchestrates differential readouts of the locus across neurons. The probability of Pcdhα promoter choice depends on enhancer/promoter encounters catalyzed by cohesin, whose extrusion trajectories determine the likelihood that an individual promoter can "escape" heterochromatin-mediated silencing. We propose that tunable locus-specific regulatory elements and cell type-specific cohesin activity underlie the generation of cellular diversity by Pcdh genes.

Using synchronized brain rhythms to bias memory-guided decisions

Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6–11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain-machine interface that initiated task trials based on the magnitude of prefrontal-hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain-machine interfacing.

Using synchronized brain rhythms to bias memory-guided decisions.

Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6-11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain-machine interface that initiated task trials based on the magnitude of prefrontal-hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain-machine interfacing.

Brain Stimulation of Dorsolateral Prefrontal Cortices Influences Impulsivity in Delay Discounting Choices.

Intertemporal decision-making is pivotal for human interests and health. Recently, studies instructed participants to make intertemporal choices for both themselves and others, but the specific mechanisms are still debated. To address the issue, in the current study, the cost-unneeded conditions (i.e., "Self Immediately - Self Delay" and "Other Immediately - Other Delay" conditions) and the cost-needed conditions (i.e., "Self Immediately - Other Delay" and "Self Delay - Other Immediately" conditions) were set with the identity of OTHER being a stranger. We manipulated the magnitude of reward (Experiment 1) and disrupted the activation of the dorsolateral prefrontal cortex with repetitive transcranial magnetic stimulation (rTMS; Experiment 2). We found that both the behavioral and rTMS manipulations increased smaller but sooner choice probability via reducing self-control function. The reduced self-control function elicited by rTMS affected both self- and other-related intertemporal choices via increasing the choice preference for smaller but sooner reward options, which may help people deeply understand the relationship between self- and other-related intertemporal choices in processing mechanism, especially when the OTHER condition is set as a stranger.

Think and Choose! The Dual Impact of Label Information and Consumer Attitudes on the Choice of a Plant-Based Analog.

This study explored the impact of various label information (extrinsic attributes) and sociodemographic and attitudinal factors (intrinsic attributes) on Brazilian consumer choices, using simulated traditional and plant-based muçarela cheese as the model product. The research was conducted in two phases: the first involved a structured questionnaire assessing attitudinal dimensions such as Health Consciousness, Climate Change, Plant-based Diets, and Food Neophobia, along with sociodemographic data collection. The second phase comprised a discrete choice experiment with (n = 52) and without (n = 509) eye tracking. The term "Cheese" on labels increased choice probability by 7.6% in a general survey and 15.1% in an eye tracking study. A prolonged gaze at "Cheese" did not affect choice, while more views of "Plant-based product" slightly raised choice likelihood by 2.5%. Repeatedly revisiting these terms reduced the choice probability by 3.7% for "Cheese" and 1% for "Plant-based product". Nutritional claims like "Source of Vitamins B6 and B12" and "Source of Proteins and Calcium" boosted choice probabilities by 4.97% and 5.69% in the general and 8.4% and 6.9% in the eye-tracking experiment, respectively. Conversely, front-of-package labeling indicating high undesirable nutrient content decreased choice by 13% for magnifying presentations and 15.6% for text. In a plant-based subsample, higher environmental concerns and openness to plant-based diets increased choice probabilities by 5.31% and 5.1%, respectively. These results highlight the complex dynamics between label information, consumer understanding, and decision-making.

Potential Cost Savings with 60-day Peripheral Nerve Stimulation Treatment in Chronic Axial Low Back Pain.

Chronic axial low back pain (CLBP) that is not responsive to medication management or physical therapy often requires significant clinical intervention. Several interventional pain management options exist, including a 60-day peripheral nerve stimulation (PNS) treatment. This economic evaluation investigated the potential for projected cost savings associated with prioritizing 60-day PNS treatment relative to a 'standard of care' (SOC) approach (where patients do not have access to 60-day PNS). A decision tree (supervised machine learning) model tracked treatment progression across two hypothetical cohorts of US patients with CLBP in whom non-interventional options were ineffective (Cohort A: treatment starting with 60-day PNS followed by any additional interventional and surgical treatments versus Cohort B: standard of care interventional and surgical treatments without access to 60-day PNS). Treatment efficacy estimates were based on published success rates. Conditional on treatment failure, up to two additional interventions were considered within the 12-month time frame in both cohorts. SOC treatment options included epidural injection, radiofrequency ablation (RFA), basivertebral nerve ablation (BVNA), PNS permanent implant (PNS-PI), spinal cord stimulator (SCS) trial/implant, and spinal fusion surgery. Treatment choice probabilities in both cohort algorithms were based on clinician interviews. Costs were based on national Medicare reimbursement levels in the ambulatory surgery center (ASC) setting. Savings reflected the difference in projected costs between cohorts. A Monte Carlo simulation and sensitivity analyses were conducted to generate confidence intervals and identify important inputs. The treatment algorithm which prioritized initial 60-day PNS treatment was projected to save $8056 (95% CI $6112-$9981) per patient during the first year of interventional treatment relative to the SOC approach. Use of the 60-day PNS treatment as an initial interventional treatment in patients with CLBP may result in significant savings for Medicare. Projected savings may be even larger for commercial payers covering non-Medicare patients.