Overview
Universal Basic Income (UBI) is defined as a transformative economic policy designed to provide all citizens with a regular, unconditional sum of money, regardless of their circumstances. Here we argue that the overlay of several modern technologies on UBI can help enhance its relevance, effectiveness, and impact. We review applications of artificial intelligence (AI) and decentralized infrastructure in UBI application, and describe prospects for a cognitive ecosystems approach towards modeling. Here we describe a version 0.1 specification for an Adaptive (Universal) Basic Income system called AuBI, describing a systems engineering-grade toolkit/sandbox/design suite for specifying, modeling, and designing economic systems. Keywords for AuBI include decentralized infrastructure, active inference modules, informed adaptive income, Bayesian community income floors, LLM, AI, data storage, data sovereignty, micro UBI, adaptive economic agents, collective predictive processing and more.
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Methods and contributions
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Findings and contributions
- The authors argue that overlaying AI, decentralized infrastructure, and Active Inference on UBI can enhance its relevance, effectiveness, and impact.
- They distinguish BOPS as using prediction to maintain static security targets, versus GPI using prediction to dynamically optimize welfare.
- For real-world implementation they suggest a staged hybrid approach, starting with BOPS and gradually transitioning toward GPI.
- The paper proposes targeted micro-UBI studies as the actionable next step, e.g. an Indonesian pilot encouraging language self-study.
Methods
- Version 0.1 system specification for an Adaptive Basic Income toolkit — Specifies AuBI as a systems-engineering toolkit/sandbox for specifying, modeling, and designing basic-income economic systems.
- Layered architecture: UX, services, AI/LLM + Active Inference, decentralized, data — Lays out five architectural layers, including an Active Inference engine using generative models and expected free energy for policy selection.
- Comparison of BOPS and GPI deployment archetypes — Contrasts a Baseline-Oriented Predictive System with Generative Predictive Integration across operational, implementation, efficiency, and context criteria.
- Indonesian micro-UBI thought experiments with projected metrics — Sketches four hypothetical Indonesian micro-studies (e.g. Dana Bahasa language fund) with budgets, data to collect, and expected benefits.
- Active Inference interpretation of UBI effects — Frames UBI as reducing environmental uncertainty and prediction errors, enabling exploratory behavior, and supporting collective predictive processing.
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