The Role of Inertia in Modeling Decisions from Experience with Instance-Based Learning

One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE). A number of computational models based upon the Instance-Based Learning Theory, a theory of DFE, have included different inertia implementations and have shown to simultaneously account for both risk-taking and alternations between alternatives. The role that inertia plays in these models, however, is unclear as the same model without inertia is also able to account for observed risk-taking quite well. This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model. We use two large datasets, estimation and competition, from the Technion Prediction Tournament involving a repeated binary-choice task to show that incorporating an inertia mechanism in an IBL model enables it to account for the observed average risk-taking and alternations. Including inertia, however, does not help the model to account for the trends in risk-taking and alternations over trials compared to the IBL model without the inertia mechanism. We generalize the two IBL models, with and without inertia, to the competition set by using the parameters determined in the estimation set. The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model

Comparative Assessment of Economic Burden of Disease in Relation to Out of Pocket Expenditure

Background: The economic costs associated with morbidity pose a great financial risk on the population. Household's over-dependence on out-of-pocket (OOP) health expenditure and their inability to cope up with the economic costs associated with illness often push them into poverty. The current paper aims to measure the economic burden and resultant impoverishment associated with OOP health expenditure for a diverse set of ailments in India.Methods: Cross-sectional data from National Sample Survey Organization (NSSO) 71st Round on “Key Indicators of Social Consumption: Health” has been employed in the study. Indices, namely the payment headcount, payment gap, concentration index, poverty headcount and poverty gap, are defined and computed. The measurement of catastrophic burden of OOP health expenditure is done at 10% threshold level.Results: Results of the study reveal that collectively non-communicable diseases (NCDs) have higher economic and catastrophic burden, individually infections rather than NCDs such as Cardio Vascular Diseases and cancers have a higher catastrophic burden and resultant impoverishment in India. Ailments such as gastro-intestinal, respiratory, musco-skeletal, obstetrics, and injuries also have a substantial economic burden on population and push them below the poverty line. Results also show that despite the pro-poor concentration of infections, their economic burden is more concentrated among the wealthier consumption groups.Conclusion: The study concludes that universal health coverage through adequate provision of pooled resources for health care and community-based health insurance is critical to reduce the economic burden and impoverishment related to OOP health expenditure. Measures should also be instituted to insulate people from economic burden on morbidity, especially the NCDs

Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods

Executive functioning is a cognitive process that enables humans to plan, organize, and regulate their behavior in a goal-directed manner. Understanding and classifying the changes in executive functioning after longitudinal interventions (like transcranial direct current stimulation (tDCS)) has not been explored in the literature. This study employs functional connectivity and machine learning algorithms to classify executive functioning performance post-tDCS. Fifty subjects were divided into experimental and placebo control groups. EEG data was collected while subjects performed an executive functioning task on Day 1. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS. On Day 10, subjects repeated the tasks specified on Day 1. Different functional connectivity metrics were extracted from EEG data and eventually used for classifying executive functioning performance using different machine learning algorithms. Results revealed that a novel combination of partial directed coherence and multi-layer perceptron (along with recursive feature elimination) resulted in a high classification accuracy of 95.44%. We discuss the implications of our results in developing real-time neurofeedback systems for assessing and enhancing executive functioning performance post-tDCS administration.Comment: 7 pages, presented at the IEEE 20th India Council International Conference (INDICON 2023), Hyderabad, India, December 202

Cash transfers, maternal depression and emotional well-being: Quasi-experimental evidence from India's Janani Suraksha Yojana programme.

Maternal depression is an important public health concern. We investigated whether a national-scale initiative that provides cash transfers to women giving birth in government health facilities, the Janani Suraksha Yojana (JSY), reduced maternal depression in India's largest state, Uttar Pradesh. Using primary data on 1695 women collected in early 2015, our quasi-experimental design exploited the fact that some women did not receive the JSY cash due to administrative problems in its disbursement - reasons that are unlikely to be correlated with determinants of maternal depression. We found that receipt of the cash was associated with an 8.5% reduction in the continuous measure of maternal depression and a 36% reduction in moderate depression. There was no evidence of an association with measures of emotional well-being, namely happiness and worry. The results suggest that the JSY had a clinically meaningful effect in reducing the burden of maternal depression, possibly by lessening the financial strain of delivery care. They contribute to the evidence that financial incentive schemes may have public health benefits beyond improving uptake of targeted health services

Classification of attention performance post-longitudinal tDCS via functional connectivity and machine learning methods

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention.Comment: 6 pages, to be presented in the IEEE 9th International Conference for Convergence in Technology (I2CT),Pune, April 2024. arXiv admin note: substantial text overlap with arXiv:2401.1770

Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods

Predicting and understanding the changes in cognitive performance, especially after a longitudinal intervention, is a fundamental goal in neuroscience. Longitudinal brain stimulation-based interventions like transcranial direct current stimulation (tDCS) induce short-term changes in the resting membrane potential and influence cognitive processes. However, very little research has been conducted on predicting these changes in cognitive performance post-intervention. In this research, we intend to address this gap in the literature by employing different EEG-based functional connectivity analyses and machine learning algorithms to predict changes in cognitive performance in a complex multitasking task. Forty subjects were divided into experimental and active-control conditions. On Day 1, all subjects executed a multitasking task with simultaneous 32-channel EEG being acquired. From Day 2 to Day 7, subjects in the experimental condition undertook 15 minutes of 2mA anodal tDCS stimulation during task training. Subjects in the active-control condition undertook 15 minutes of sham stimulation during task training. On Day 10, all subjects again executed the multitasking task with EEG acquisition. Source-level functional connectivity metrics, namely phase lag index and directed transfer function, were extracted from the EEG data on Day 1 and Day 10. Various machine learning models were employed to predict changes in cognitive performance. Results revealed that the multi-layer perceptron and directed transfer function recorded a cross-validation training RMSE of 5.11% and a test RMSE of 4.97%. We discuss the implications of our results in developing real-time cognitive state assessors for accurately predicting cognitive performance in dynamic and complex tasks post-tDCS interventionComment: 16 pages, presented at the 30th International Conference on Neural Information Processing (ICONIP2023), Changsha, China, November 202

Effect of a multifaceted social franchising model on quality and coverage of maternal, newborn, and reproductive health-care services in Uttar Pradesh, India: a quasi-experimental study.

BACKGROUND: How to harness the private sector to improve population health in low-income and middle-income countries is heavily debated and one prominent strategy is social franchising. We aimed to evaluate whether the Matrika social franchising model-a multifaceted intervention that established a network of private providers and strengthened the skills of both public and private sector clinicians-could improve the quality and coverage of health services along the continuum of care for maternal, newborn, and reproductive health. METHODS: We did a quasi-experimental study, which combined matching with difference-in-differences methods. We matched 60 intervention clusters (wards or villages) with a social franchisee to 120 comparison clusters in six districts of Uttar Pradesh, India. The intervention was implemented by two not-for-profit organisations from September, 2013, to May, 2016. We did two rounds (January, 2015, and May, 2016) of a household survey for women who had given birth up to 2 years previously. The primary outcome was the proportion of women who gave birth in a health-care facility. An additional 56 prespecified outcomes measured maternal health-care use, content of care, patient experience, and other dimensions of care. We organised conceptually similar outcomes into 14 families to create summary indices. We used multivariate difference-in-differences methods for the analyses and accounted for multiple inference. FINDINGS: The introduction of Matrika was not significantly associated with the change in facility births (4 percentage points, 95% CI -1 to 9; p=0·100). Effects for any of the other individual outcomes or for any of the 14 summary indices were not significant. Evidence was weak for an increase of 0·13 SD (95% CI 0·00 to 0·27; p=0·053) in recommended delivery care practices. INTERPRETATION: The Matrika social franchise model was not effective in improving the quality and coverage of maternal health services at the population level. Several key reasons identified for the absence of an effect potentially provide generalisable lessons for social franchising programmes elsewhere. FUNDING: Merck Sharp and Dohme Limited

Explainable Digital Creatives Performance Monitoring using Deep Feature Attribution

A key challenge in marketing and advertising research is understanding when and why digital assets such as promotional content perform well during a marketing push. By leveraging raw image feature vectors extracted from large datasets, we can train performance prediction models using online social signals such as likes or views. While the resulting models make accurate predictions, they are opaque and rely on abstract features within the model, making attribution almost impossible. This paper demonstrates an approach to performance prediction modelling for image based digital creative assets. Utilising a combination of pre-trained vision model embeddings with a pipeline of generative Artificial Intelligence (AI) for image synthesis and manipulation, we establish a means of determining the performance of explainable components. This enables flexible performance prediction, even with smaller datasets, with high degree of explainability through the attribution of image features correlating with high or low performance

Matrika Household Survey in India

Data produced as part of a study to evaluate the impact of the Matrika social franchising model – a multi-faceted intervention that established a network of private providers and strengthened the skills of both public and private sector clinicians – and determine whether it has improved the quality and coverage of health services along the continuum of care for maternal, newborn and reproductive health in Uttar Pradesh, India. The datasets cover two rounds of a household survey, performed in January 2015 and May 2016, of women who had recently given birth

The effect of report cards on the coverage of maternal and neonatal health care: a factorial, cluster-randomised controlled trial in Uttar Pradesh, India.

BACKGROUND: Report cards are a prominent strategy to increase the ability of citizens to express their view, improve public accountability, and foster community participation in the provision of health services in low-income and middle-income countries. In India, social accountability interventions that incorporate report cards and community meetings have been implemented at scale, attracting considerable policy attention, but there is little evidence on their effectiveness in improving health. We aimed to evaluate the effect of report cards, which contain information on village-level indicators of maternal and neonatal health care, and participatory meetings targeted at health providers and community members (including local leaders) on the coverage of maternal and neonatal health care in Uttar Pradesh, India. METHODS: We conducted a repeated cross-sectional, 2 × 2 factorial, cluster-randomised controlled trial, in which each cluster was a village (rural) or ward (urban). The clusters were randomly assigned to one of four groups: the provider group, in which we shared report cards and held participatory meetings with providers of maternal and neonatal health services; the community group, in which we shared report cards and held participatory meetings with community members (including local leaders); the providers and community group, in which report cards were targeted at both health providers and the community; and the control group, in which report cards were not shared with anyone. We generated these report cards by collating data from household surveys and shared the report cards with the recipients (as determined by their assigned groups) in participatory meetings. The primary outcome was the proportion of women who had at least four antenatal care visits (ie, attended a clinic or were visited at home by a health-care worker) during their last pregnancy. We measured outcomes with cross-sectional household surveys that were taken at baseline, at a first follow-up (after 8 months of the intervention), and at a second follow-up (21 months after the start of the intervention). Analyses were by intention to treat. This trial is registered with ISRCTN, number ISRCTN11070792. FINDINGS: We surveyed eligible women for the baseline survey between Jan 13, and Feb 5, 2015. We then randomly assigned 44 clusters to the provider group, 45 clusters to the community group, 45 clusters to the provider and community group, and 44 clusters to the control group. Report cards of collated survey data were provided to recipient groups, as per their random allocation, in October, 2015, and in September, 2016. We ran the first follow-up survey between May 16 and June 10, 2016. We ran the second follow-up survey between June 18 and July 18, 2017. We measured the primary outcome in 3133 women (795 in the provider group, 781 in the community group, 798 in the provider and community group, and 759 in the control group) who gave birth during implementation of the intervention, between Feb 1, 2016, and July 18, 2017 (the end of the second follow-up survey). The report card intervention did not significantly affect the proportion of women who had at least four antenatal care visits (provider vs non-provider: odds ratio 0·85, 95% CI 0·65-1·13; community vs non-community: 0·86, 0·65-1·13). INTERPRETATION: Maternal health report cards containing information on village performance, targeted at either the community or health providers, had no detectable effect on the coverage of maternal and neonatal health care. Future research should seek to understand how the content of information and the delivery of report cards affect the success of this type of social accountability intervention. FUNDING: Merck Sharp and Dohme