arXiv subjects

Jatin Alla

Publications and source records attributed to Jatin Alla.

2 recordsLinked to original sources

arXiv2026

AI as a Democratizing Force in Indie Game Development

The video game industry of 2024-2026 shows the deepest AAA-level contraction in its modern history alongside the largest-ever expansion of independent output. We examine AI's role in that divergence across four research questions, using public marketplace data, a title-level Steam catalog dataset cross-referenced with Steam's generative-AI disclosure records, and a fourteen-month log from an agentic AI game-production platform. RQ1 (barriers to entry): production planning, historically a salaried producer role at roughly $59 per hour, is generated in a mean of 5.1 minutes for $0.27-0.58 per plan. We operationalize "democratization" across seven dimensions and claim it for one, coordination cost, repriced by roughly four orders of magnitude; the regional dimension is argued from cost arithmetic, not measured. RQ2 (market acceptance): indie volume and unit sales expanded, AI-disclosed releases rose eightfold in eighteen months, professional sentiment collapsed across three surveys, and disclosed releases received catalog-typical reception (median 85.9 percent positive) in a verified subsample. RQ3 (a new wave): the moment resembles a third democratization wave after distribution (2008-2012) and construction (2013-2020); four pre-registered convergence tests align, and as production cost nears zero while mature-market demand contracts, the market shows preconditions of structural oversupply, forcing a new distribution paradigm. RQ4 (quality): the platform log measures plan generation, not shipped-game quality, so we compare quality signals for AI-disclosed versus non-disclosed titles; disclosure records AI use in production, not authorship. Releases doubled from 9,654 (2020) to over 20,000 (2025) while only about 300 titles grossed above $1 million, and participation in major markets fell below pre-pandemic levels; we close with implications for developers, educators, and platform design.

cs.CY↗

arXiv2025

A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial

Consistent physical inactivity poses a major global health challenge. Mobile health (mHealth) interventions, particularly Just-in-Time Adaptive Interventions (JITAIs), offer a promising avenue for scalable, personalized physical activity (PA) promotion. However, developing and evaluating such interventions at scale, while integrating robust behavioral science, presents methodological hurdles. The PEARL study was the first large-scale, four-arm randomized controlled trial to assess a reinforcement learning (RL) algorithm, informed by health behavior change theory, to personalize the content and timing of PA nudges via a Fitbit app. We enrolled and randomized 13,463 Fitbit users into four study arms: control, random, fixed, and RL. The control arm received no nudges. The other three arms received nudges from a bank of 155 nudges based on behavioral science principles. The random arm received nudges selected at random. The fixed arm received nudges based on a pre-set logic from survey responses about PA barriers. The RL group received nudges selected by an adaptive RL algorithm. We included 7,711 participants in primary analyses (mean age 42.1, 86.3% female, baseline steps 5,618.2). We observed an increase in PA for the RL group compared to all other groups from baseline to 1 and 2 months. The RL group had significantly increased average daily step count at 1 month compared to all other groups: control (+296 steps, p=0.0002), random (+218 steps, p=0.005), and fixed (+238 steps, p=0.002). At 2 months, the RL group sustained a significant increase compared to the control group (+210 steps, p=0.0122). Generalized estimating equation models also revealed a sustained increase in daily steps in the RL group vs. control (+208 steps, p=0.002). These findings demonstrate the potential of a scalable, behaviorally-informed RL approach to personalize digital health interventions for PA.

cs.LG↗