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NICA: The Neuro-Inspired Computational Architecture for Behavioral Decisions

Sat, 28 Mar 2026 00:00:00 +0000

How does the human brain bridge the gap between a neural signal in the prefrontal cortex and an empirical choice recorded in a survey?

Introducing NICA (Neuro-Inspired Computational Architecture), a conceptual and computational framework I’ve developed to unify my PhD research findings into a single, cohesive model of decision-making.

The Logic of NICA

At its core, NICA is a multi-layered architecture that simulates how humans evaluate trade-offs between waiting time and clinical efficacy. It consists of three primary modules:

Value Integration (vmPFC): The ventromedial Prefrontal Cortex (vmPFC) acts as the “value hub.” Here, I represent the utility of a vaccine not as a fixed number, but as a dynamically integrated signal that factors in clinical results and the “cost of delay.”
Evidence Accumulation (DDM): The choice is modeled as a Drift Diffusion Model (DDM). The “drift rate” (the speed at which we move toward a decision) is directly modulated by the neural value signal from the vmPFC.
Parameter Injection (Empirical): This is where the NICA framework becomes powerful. It takes the real-world parameters from my dissertation:
- $\kappa = 0.225$: The scaling factor for wait-time sensitivity.
- $\delta = 0.160$: The hyperbolic discounting rate derived from 1,027 respondents in Wuhan.

Validating NICA

When I injected these empirical values into the NICA simulation, the architecture produced a Behavioral Decision Metric (BDM) of 1.29. This value remarkably aligns with the observed choice patterns in the survey data, effectively “closing the loop” between neural architecture and behavioral outcomes.

Furthermore, NICA’s “decoding” of the decision process yields a Monthly Willingness to Accept (MWTA) of 44.8 RMB/month. This provides a concrete economic value for the psychological burden of a one-month delay.

Why This Matters for Behavioral AI

NICA isn’t just a model of human behavior; it’s a blueprint for Behavioral AI Architectures.

As we move toward agents that can truly model human preferences—like the “Digital Familiars” we’re building in OpenClaw—architectures like NICA will be essential. They allow us to create AI that doesn’t just “predict” choice, but “understands” the underlying neural and economic friction that drives it.

Stay tuned as I further refine the NICA framework and integrate it into my future research at UC Berkeley and beyond.

Embodied AI & Behavioral Modeling: From Unitree G1 to Smart Grid Optimization

Tue, 24 Mar 2026 00:00:00 +0000

Is it possible to “run” a behavioral economics model on a robot?

As I prepare for my new Mac Studio M4 Max (128GB) to arrive in May, my research is pivoting toward Embodied AI. This isn’t just about robotics; it’s about the micro-foundation of decision-making in autonomous systems.

1. The Idea: Behavioral Models as “Firmware”

Most robotics today focuses on perception and navigation. But what if we encode Behavioral Economics models (like prospect theory or hyperbolic discounting) directly into the decision logic of a robot?

Unitree G1 / XLeRobot: These platforms aren’t just toys. They are “embodied agents.”
The Experiment: If a robot has to choose between an immediate “low-energy” task and a delayed “high-reward” task, how does it weigh them? By implementing Behavioral Modeling, we can simulate how human-like biases might affect autonomous agents in complex environments.

2. The Princeton PPPP Connection: Smart Grids

This might sound like pure engineering, but it has profound implications for Energy Policy and Smart Grids:

Autonomous Energy Optimization: Future energy systems won’t be managed by humans in real-time. They will be managed by a mesh of autonomous agents—from smart thermostats to EV chargers.
Micro-Foundations: In energy modeling, we often assume these agents are “perfectly rational.” But what if they aren’t? By studying Embodied AI Behavioral Decision Logic, we can build more resilient energy models that account for “irrational” or “constrained” agent behavior in smart grids.
The Goal: Integrating these micro-level behavioral insights into the Peking-Princeton Project (PPPP) macro-energy models.

3. Future Academic Calendar: A New Chapter

As of today, March 24, 2026, I am at a crossroads. Tomorrow, March 25, marks the start of the UCSD Spring Quarter. This is the final sprint before my defense on April 14.

The transition from a “pure” behavioral economist to an “embodied AI” researcher mirrors the transition from a PhD student to a postdoc. The next few months will be dedicated to:

FODP: Finalizing my dissertation slides (aiming for 20+ slides by tomorrow).
Hardware Prep: Setting up the Mac Studio for local LLM and robotics simulation.
Princeton/Stanford Transition: Finalizing applications that bridge these two worlds.

This is the fourth installment in a series exploring the future of behavioral economics. The journey from Wuhan’s vaccination clinics to the autonomous grids of the future continues.

Intertemporal Choice & Hyperbolic Discounting: From Vaccines to Energy Policy

Tue, 24 Mar 2026 00:00:00 +0000

Individual decision-making often suffers from a fundamental conflict: The present self wants comfort, while the future self needs security.

This struggle, central to Intertemporal Choice, manifests in everything from public health to global energy transitions. At its heart lies Hyperbolic Discounting.

1. The Core Concept: Hyperbolic Discounting

In classical economics, we assume people discount the future at a constant, exponential rate. In reality, we are much more “present-biased.”

We overvalue immediate rewards and overstate immediate costs. This leads to a decision failure where we choose a smaller, sooner benefit (like avoiding a 30-minute queue or a small tax) over a much larger, later gain (like long-term immunity or a stable climate).

2. From Vaccination to Energy Policy

My recent research on Waiting Time as a Behavioral Barrier in vaccination shows how even a 3-month delay in protection leads to a “utility cliff.” This is a classic example of hyperbolic discounting: the short-term cost of waiting outweighs the massive long-term benefit of health.

But this model extends far beyond health. Consider Energy Policy:

The Problem: Energy transitions (like shifting to renewables or nuclear) are typical “current cost, future benefit” models.
The Bias: Implementing green energy requires significant upfront investment and potential short-term price hikes. The benefits—reduced emissions and energy independence—are decades away.
The Short-sighted Path: Because of hyperbolic discounting, both voters and policymakers tend to favor cheaper, fossil-fuel-based energy paths that provide immediate relief but catastrophic long-term costs.

3. The Princeton PPPP Connection

As I look toward the Peking-Princeton Project (PPPP) and related energy policy research at Princeton, my goal is to introduce these behavioral models into energy systems modeling.

By quantifying the “behavioral discount rate” of different demographics, we can design policies that “nudge” society toward long-term thinking. Whether it’s a vaccine or a power plant, the challenge remains the same: Helping our present selves make choices our future selves will thank us for.

This is the second installment in a series exploring the intersection of behavioral economics and public policy. Join me on April 14, 2026, for my PhD dissertation defense!

Trust Decay in Infrastructures: When Policy Meets Polarized Information

Tue, 24 Mar 2026 00:00:00 +0000

In traditional policy modeling, we often operate under a dangerous assumption: Perfect Execution. We assume that if a policy is scientifically sound and economically efficient, it will be adopted and followed.

However, as we navigate increasingly complex digital and social landscapes, a hidden variable is emerging as the ultimate gatekeeper of success: Institutional Trust.

1. The Decay of Trust in Digitalized Environments

My ongoing research, particularly aligned with my interests at the UC Berkeley I School, focuses on the evolution of public trust within changing information environments.

Trust is not static; it decays. In a world of algorithmic filter bubbles and strategic misinformation, the “decay rate” of trust in public institutions (like health departments or energy regulators) has accelerated. When people lose trust in the source of information, they don’t just ignore the message—they often adopt the opposite behavior as a form of resistance.

2. The “Trust Parameter” in Energy Modeling

This is where the theoretical meets the practical. In energy systems modeling—a key focus for my future work with the Princeton PPPP—models typically assume that carbon taxes or renewable subsidies will result in predictable shifts in consumer behavior.

But what if we introduce a Trust Parameter?

The Misinformation Loop: How does misinformation about climate change or nuclear safety alter the perceived risk-benefit ratio?
Policy Failure: In a polarized society, even a financially beneficial energy policy can fail if it’s perceived as an “elite-driven” agenda.
Improving Predictability: By quantifying how trust fluctuations—driven by digital information interventions—impact policy compliance, we can build models that are not just mathematically elegant, but sociologically robust.

3. Future Academic Outlook

As I move toward my dissertation defense and subsequent postdoctoral research, my “Future Academic Calendar” is centered on this intersection: How can we design resilient public infrastructures that account for the fragility of human trust?

Whether we are fighting a pandemic or a climate crisis, the most critical infrastructure we must maintain is not made of steel or fiber optics—it’s the invisible bond of trust between the state and its citizens.

Part 3 of my series on Behavioral Economics and Public Policy. My PhD dissertation defense is scheduled for April 14, 2026.

Waiting Time as a Behavioral Barrier: Unmasking Procrastination in Vaccination

Tue, 24 Mar 2026 00:00:00 +0000

In the realm of public health, we often focus on safety and efficacy as the primary drivers of vaccine uptake. However, our recent research reveals a hidden, yet potent structural barrier: Waiting Time.

The “Cliff” of Procrastination

Are humans rational “exponential discounters,” or are we impulsive “hyperbolic discounters”?

Using a Mixed Logit model, we analyzed the decision-making process behind vaccination timing. The results were striking. Instead of a gradual decline in utility over time, we found what can only be described as a “behavioral cliff.”

If you ask someone to wait 3 months for a vaccine, their willingness to get vaccinated doesn’t just dip—it drops significantly. This validates the presence of Present Bias ($\kappa = 1.056$).

Key Findings

Discounting is Non-Linear: The perceived disutility of a 3-month delay is comparable to a substantial reduction in the vaccine’s clinical efficacy.
Institutional Trust Matters: Individuals with lower trust in institutions exhibit significantly higher sensitivity to service delays. For them, waiting isn’t just an inconvenience; it’s a psychological burden.
Vulnerability Heterogeneity: Older populations and those with higher health concerns surprisingly show different delay-sensitivity patterns, often linked to their perceived “escape” from risk.

Policy Implications: “Time-to-Protection”

The takeaway for policymakers is clear: Time is a metric of performance.

To increase uptake, we must minimize the “time-to-protection” as much as we maximize clinical results. Vaccination campaigns should be as much about logistics and immediate accessibility as they are about science communication.

This post is based on my Job Market Paper (JMP) and upcoming PhD dissertation defense. Stay tuned for more insights as I prepare for my Final Oral Examination on April 14, 2026.

Uses

Tue, 24 Oct 2023 00:00:00 +0000

The idea of a Uses page is to tell you about the stuff I use.

Make sure to check out for a list of everyone’s Uses pages!

I often get asked about what software or hardware I use, so this page will serve as a living document and a place to point curious readers to when I get asked.

Website

(see the tutorial on )

Editor + Terminal

is my current editor
Chrome is my main browser
iTerm2 is my terminal

Desktop Apps

Better Touch Tool for window management and custom keyboard shortcuts