The DICE MODEL Deception
A Climate Model Built for the 1990's Advancing Agenda 2030
Institutional Climate Governance Is Operating on Incomplete Reality Models.
The DICE model (“Dynamic Integrated Climate-Economy”) was originally developed by economist William Nordhaus in the early 1990s as an integrated assessment model linking:
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economic growth,
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carbon emissions,
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climate impacts,
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and policy cost projections.
At the time, the DICE model was developed in the 1990s by a single economist as a simplified tool for climate-economic modeling. It was never designed for the complexity of today’s world. It assumes stable relationships between variables that no longer hold in an era dominated by artificial intelligence, exploding data center energy consumption, and rapidly shifting technological landscapes.
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Cloud computing did not exist.
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Artificial intelligence infrastructure did not exist.
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Modern hyperscale data centers did not exist.
Traditional actuarial risk models like DICE create a dangerous illusion of precision. They fail to account for the massive new drivers of risk — particularly the unprecedented electricity demand from AI infrastructure and the non-linear feedback loops created by machine learning systems. This blindness is not a minor limitation; it is a structural flaw that renders the model increasingly unreliable for long-term forecasting.
The problem is structural: DICE was built for a linear industrial economy.
The modern world is increasingly governed by:
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exponential compute growth,
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machine learning infrastructure,
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automated decision systems,
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algorithmic governance,
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and AI-driven electricity demand.
These variables are not peripheral anymore.
They are becoming central drivers of:
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AI governance
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data quality,
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legalease,
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UN Agenda 2030
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actuarial liability
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and ethical AI
Yet legacy climate-economic models remain largely blind to this transformation.
Actuaries must urgently recognise that continuing to rely on outdated models like DICE is not just technically questionable — it is professionally irresponsible. The integrity of our entire profession depends on our willingness to challenge models that no longer reflect reality.
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AI Governance
A core tool for climate risk modeling.
The IAA and SOA’s SDG Task Forces, formed in 2024, selected the DICE model as a core tool for climate risk modeling.
The Problem
DICE was created in the 1990s by a single economist. It was never designed for today’s world.
This outdated model has no variables for data centers, AI energy consumption, or the explosion of electricity demand. It is completely blind to one of the largest new sources of emissions on the planet.
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02
Data Quality
Personal Impact: Food Access & Affordability
Agricultural yield food systems are increasingly managed through supply-chain optimisation, sustainability targets, and risk-based allocation models. These frameworks can affect food availability, pricing, and access at the local level.
What You Experience:
Higher food prices, reduced choice, or increased dependence on regulated distribution systems - especially during shortages, disruptions, or policy transitions.
03
Legalese - Words of Art
Personal Impact: Unknown Contractual Traps
Insurance contracts are being rewritten in deceptive legal language. “Words of art” hide liabilities and shift risk onto policyholders — often without their knowledge.
What You Experience:
Actuaries have a critical role in protecting the public from these invisible contractual traps.
04
UN Agenda 2030 ESG & SDG Goals
Personal Impact: 17 Goals based on SDG, ESG & DEI Ideologies
Political agendas are increasingly corrupting actuarial assumptions. Climate risk modeling, diversity targets, and sustainability mandates are pressuring actuaries to manipulate numbers rather than report reality.
What You Experience:
Shifts in preserved reality and an increased pressure for children to align with predefined skills or economic outcomes.
05
Actuarial Liability Explosion
Personal Impact: The removal of Human Center Systems
AI-driven decisions in autonomous vehicles, health scoring, credit, and insurance are creating massive long-term liabilities. Current reserving practices may be dangerously inadequate.
What You Experience:
Home, workplace and life decisions are shaped by Social Credit Scoring metrics rather than individual merit, with limited transparency into how classifications affect opportunity or advancement.
06
Ethical AI vs Profit Drive AI
Personal Impact: An Unethical Society
Actuaries face a clear choice: stay silent and become complicit, or step up as the ethical backbone of the insurance industry. The profession must build new standards before it’s too late.
What You Experience:
Tiered pricing, usage restrictions, or sudden changes to access during droughts or “stress” conditions - often accompanied by rising utility costs.