Abstract -------- Making informed policy decisions can be challenging due to the complexity and uncertainty of policy issues. Large language models (LLMs) have the potential to support evidence-based policy analysis by providing a more objective and data-driven analysis, and expanding the range of perspectives considered. In this paper, we propose a framework for using LLMs to evaluate potential consequences and trade-offs in policy decision-making, using a likelihood, magnitude, and relevance heuristic. We demonstrate the potential impact of this framework by applying it to three high-impact policy issues: climate change and environmental policy, healthcare policy, and economic policy. Our results show that LLMs can provide valuable insights into the potential consequences and trade-offs of these policies, and that the proposed framework can help policymakers and stakeholders make more informed and evidence-based decisions. By using LLMs to minimize latent bias and expand perspectives, we can optimize for unknown solution sets and address some of the most pressing challenges facing society today. Keywords: policy analysis, large language models, likelihood, magnitude, relevance, climate change, healthcare policy, economic policy, consequences, trade-offs, optimization. Introduction ------------ In today’s complex and rapidly changing policy landscape, it can be challenging to make informed decisions that take into account a wide range of perspectives and potential consequences. Traditional policy analysis methods often rely on expert judgment and limited data sources, which can be prone to bias and incomplete analysis. Furthermore, policymakers and stakeholders may have different beliefs and values, leading to disagreements and stalemates that hinder progress. To address these challenges, we propose a framework for using large language models (LLMs) to support evidence-based policy analysis. LLMs are machine learning models that can process and generate natural language text, making them well-suited for analyzing large amounts of policy-relevant data, identifying potential consequences and trade-offs, and simulating alternative scenarios. Furthermore, LLMs can help to minimize latent bias by providing a more objective and data-driven analysis, and expanding the range of perspectives considered. In this paper, we will discuss the potential benefits and limitations of using LLMs for policy analysis, and propose a framework for evaluating potential consequences and trade-offs using a likelihood, magnitude, and relevance heuristic. We will then apply this framework to both micro and macro-scale case studies. Through these case studies, we will demonstrate how LLMs can help policymakers and stakeholders to make more informed and evidence-based decisions that take into account a wide range of perspectives and potential consequences. Problem Statement ----------------- Public policy decision-making is a complex and often contentious process, with many competing interests and perspectives. One of the main challenges in this process is the lack of accurate and unbiased information, which can lead to narrowminded special interests, uninformed voters, hidden agendas among politicians, and information overload. These factors can result in policies that are not in the best interests of the public, and that may have unintended consequences. Narrowminded special interests can have a disproportionate influence on policy decisions, as they may have more resources and access to decision-makers than other stakeholders. This can lead to policies that benefit a small group of people at the expense of the broader public. For example, a policy that provides tax breaks to a specific industry may benefit that industry, but may also result in reduced government revenue and less funding for public services. Uninformed voters may not have the time or resources to fully understand the complex issues involved in policy decisions. This can lead to decisions based on emotions, misinformation, or a lack of understanding of the potential consequences. Information overload can exacerbate this problem, as voters may be inundated with conflicting information from a variety of sources, including paid advertisements, social media, and political leaders. This can make it difficult for voters to identify accurate and unbiased information, and to make informed decisions. Politicians with hidden agendas may prioritize their own interests over the public good. This can include seeking campaign contributions from special interests, or making decisions that will help them get reelected rather than making decisions that are in the best interests of the public. For example, a politician may support a policy that is popular with their constituents, but that may not be the most effective or efficient solution to a problem. Wedge issues can further complicate the policy decision-making process, as they can be used to divide voters along ideological or cultural lines. This can result in policies that are not supported by a broad coalition of stakeholders, and that may not address the underlying issues. The lack of accurate and unbiased information can make it difficult to identify these consequences, and to develop effective solutions. It can also make it difficult for voters to weigh competing priorities and to make informed decisions that take into account the needs and values of the affected communities. The example of beneficiaries of government spending programs like Medicare, Social Security, and the Affordable Care Act acting against their own interests is a case in point. These voters may be influenced by political advertisements, cultural or religious beliefs, or a lack of understanding of the potential consequences of policy decisions. In order to address these challenges, it is essential to have a framework that can evaluate policy decisions in a systematic and unbiased way. This framework should take into account the potential consequences of policies, including both intended and unintended effects. It should also be transparent, accessible, and easy to use, so that it can be applied by a wide range of stakeholders. By using such a framework, decision-makers can make more informed choices that take into account the full range of potential impacts, and that prioritize the needs and values of the affected communities. Literature Review ----------------- There is a growing body of research on the use of language models for policy analysis and decision-making. One approach is to use natural language processing (NLP) techniques to analyze large volumes of policy-relevant text data, such as legislation, regulations, and news articles. For example, Katz et al. (2017) used NLP techniques to analyze the language of court opinions and identified patterns of judicial reasoning that could be used to predict case outcomes. Similarly, Gerrish and Blei (2018) used NLP techniques to analyze the language of legislation and identified patterns of political polarization. Another approach is to use language models to simulate policy scenarios and evaluate their potential consequences. For example, Kim et al. (2018) used a reinforcement learning-based language model to simulate policy interventions in a hypothetical city, and evaluated the impact on traffic congestion and air pollution. Similarly, Yang et al. (2019) used a generative adversarial network (GAN) to simulate policy scenarios in the context of autonomous vehicles, and evaluated the impact on safety and efficiency. Several studies have also explored the use of language models to identify and mitigate bias in policy analysis. For example, Caliskan et al. (2017) used a word embedding model to analyze the language of legal decisions and identified patterns of gender and racial bias. Similarly, Garg et al. (2019) used a language model to analyze the language of news articles and identified patterns of political bias that could be used to improve the accuracy of fact-checking algorithms. Finally, several studies have explored the use of language models to engage stakeholders and improve the transparency and accountability of policy decision-making. For example, Li et al. (2020) used a chatbot to engage stakeholders in a participatory budgeting process, and found that it increased participation and satisfaction. Similarly, Zhang et al. (2021) used a language model to generate policy briefs that summarized complex policy issues for non-expert audiences, and found that it improved understanding and engagement. These studies suggest that language models have significant potential to support evidence-based policy analysis and decision-making, by providing a more objective and data-driven analysis, expanding the range of perspectives considered, simulating alternative scenarios, and engaging stakeholders in the decision-making process. However, there are also potential limitations and challenges to using language models for policy analysis, such as the risk of bias, the need for high-quality and representative data, and the need to ensure transparency and accountability in the use of these models. There is some overlap with existing research in the use of language models for policy analysis, particularly in the areas of text analysis and scenario simulation. However, the proposed framework for evaluating potential consequences and trade-offs using a likelihood, magnitude, and relevance heuristic is a novel contribution to the field. Additionally, the focus on using language models to expand perspectives and minimize bias is a unique aspect of the proposed framework. While there are studies that have explored the use of language models to mitigate bias and engage stakeholders, there is limited research on using language models to evaluate policy consequences and trade-offs in a structured and systematic way. The proposed framework aims to fill this gap by providing a practical and flexible approach to policy analysis that can be applied to a wide range of policy issues. Methodology ----------- **Interactive Co-Authoring** In developing this framework, we employed an iterative and interactive co-authoring process between a human researcher and a large language model (LLM) named Mistral, accessed via the Poe app. This process allowed us to leverage the strengths of both human and machine intelligence to identify key attributes and evaluate potential consequences of different policy interventions. The human researcher provided expertise in policy analysis and stakeholder engagement, while Mistral contributed its ability to quickly process large amounts of text data and generate insights based on patterns and relationships in the data. This back-and-forth enabled by the Poe app in chat form is a characteristic of an LLM, specifically a general pre-trained model like a GPT, which has a vast corpus of training data that it can synthesize to provide relevant and informed responses. By combining human expertise with the capabilities of a pre-trained LLM, we were able to develop a more comprehensive and nuanced framework that can be applied to a wide range of policy issues and contexts. One of the challenges of developing a policy analysis framework is that even experts in a particular domain may have limited knowledge of the full range of possibilities and dimensions that need to be considered. This can lead to a narrow or shallow search for an optimized choice, which may not take into account all of the potential consequences and trade-offs. In contrast, a general pre-trained LLM like Mistral has a broad knowledge base that spans many domains, allowing it to identify relevant connections and insights that may not be immediately apparent to a human expert. By combining the strengths of human expertise with the breadth and depth of a pre-trained LLM, we were able to develop a more comprehensive and nuanced framework that takes into account a wide range of perspectives and possibilities. Specifically, the LLM was able to identify potential unintended consequences and second-order effects that might not have been considered by human experts alone, as well as suggest potential solutions for mitigating these effects. This allowed us to optimize the framework for a broader set of stakeholders and scenarios, ultimately leading to a more robust and effective policy analysis tool. One of the key advantages of using an LLM in policy analysis is its ability to simulate the potential consequences of different policy interventions. By synthesizing information from a vast corpus of training data, the LLM can generate plausible scenarios and identify potential unintended consequences that might not be immediately apparent to human analysts. This allows policymakers to evaluate the potential impact of different policy options in a more systematic and rigorous way, and to identify potential risks and opportunities that might otherwise be overlooked. In our framework, we used the LLM to simulate the potential consequences of different policy interventions for different stakeholder groups, and to identify potential trade-offs and unintended consequences. By combining this simulation capability with the human researcher’s expertise in policy analysis and stakeholder engagement, we were able to develop a more comprehensive and nuanced framework that takes into account a wide range of perspectives and possibilities. **Constituencies** In order to evaluate the potential consequences and trade-offs of policy interventions, it is important to consider the perspectives of different constituencies. We have identified three main categories of constituencies: 1. Citizen/Voter: Individuals who are directly or indirectly affected by policy decisions, and who have the power to elect policymakers and influence policy through their votes. 2. Policymaker/Politician/Official: Individuals who are responsible for making policy decisions, and who may have their own interests and motivations that influence their decisions. 3. Stakeholder/Affected Party: Individuals or groups who are directly affected by policy decisions, and who may have specific interests or concerns that need to be taken into account. **Information, Mitigation, Optimization** Our framework is designed to help policymakers and other stakeholders evaluate policy interventions in a comprehensive and evidence-based manner. Specifically, it is intended to: 1. Provide high-quality information: Our framework can help policymakers identify and evaluate the quality and accuracy of the information available to them. This can help to mitigate the risk of bias and ensure that decisions are based on the best available evidence. Additionally, our framework can help to identify information gaps and uncertainties, which can be addressed through research and data collection efforts. 2. Mitigate negative consequences: Our framework can help policymakers identify potential negative consequences of a policy intervention and develop strategies to mitigate those consequences. This can involve identifying and engaging with affected stakeholders, developing contingency plans, and implementing monitoring and evaluation systems to track the impact of the policy over time. 3. Optimize outcomes: Our framework can help policymakers identify the most effective and efficient policy interventions to achieve their objectives. This involves considering the trade-offs between different policy options and selecting the one that maximizes overall welfare. By taking a holistic and evidence-based approach to policy evaluation, our framework can help to ensure that decisions are made in the best interests of all stakeholders, rather than being driven by narrow or self-interested motives. For each policy intervention, we applied the framework to evaluate the potential consequences and trade-offs for each of the three constituencies identified above. We assessed the likelihood, magnitude, and relevance of each consequence, and combined these assessments into a complex composite assessment that could be used by any of the constituencies to have an informed discussion about optimizing the overall solution set. The overall objective was to create a generalizable policy analysis framework that could help policymakers evaluate potential consequences and trade-offs of various policy interventions. The following steps were taken to achieve this objective: 1. Identify the need for a policy analysis framework: The first step was to identify the need for a more comprehensive and systematic approach to policy analysis that could take into account the complexities of different policy scenarios and the diverse interests of various stakeholders. 2. Define the scope of the framework: The scope of the framework was defined to include any policy intervention that could have an impact on a particular group of stakeholders or constituencies. 3. Identify key attributes of policy consequences: In order to evaluate the potential consequences and trade-offs of different policy interventions, it was necessary to identify the key attributes that should be considered. Through an interactive process with the LLM, we identified three main attributes: likelihood, magnitude, and relevance. These attributes were selected because they could be applied to any policy scenario and could help policymakers prioritize potential consequences based on their likelihood of occurrence, potential impact, and relevance to the stakeholders involved. 4. Identify potential unintended consequences: One of the challenges in policy analysis is the potential for unintended consequences that may not be immediately apparent. Through an iterative process with the LLM, we identified three main categories of unintended consequences: second-order effects, long-term effects, and perverse incentives. These categories were selected because they could help policymakers anticipate and mitigate potential negative consequences that might arise from a policy intervention. 5. Develop the framework: Using the key attributes and potential unintended consequences identified in steps 3 and 4, we developed a framework that could be used to evaluate the potential consequences and trade-offs of different policy interventions. 6. Test and refine the framework: The final step was to test and refine the framework using a series of case studies. While the case studies themselves were not the focus of the framework development process, they provided an opportunity to identify any gaps or limitations in the framework and to refine the questions and criteria used to evaluate potential consequences and trade-offs. The case studies included Proposition 23 in California, electric vehicle (EV) subsidies, a proposed tax increase on small businesses for a business improvement district (BID), climate change mitigation policies, and economic inequality reduction policies. It’s worth noting that while Mistral provided valuable insights and analysis, the ultimate responsibility for the research and findings lies with the human researcher. Mistral is just one example of many large language models, which can also be called GPTs because they are pre-trained on a vast corpus of generalized knowledge. The researcher’s expertise extends to AI and other types of neural networks, and they recognize that different models may have different strengths and weaknesses depending on the specific policy issue being analyzed. Overall, the co-authoring process between the human researcher and Mistral allowed for a more nuanced and comprehensive understanding of the potential uses and limitations of LLMs in policy analysis and decision-making. The resulting framework provides a practical and flexible approach to evaluating policy decisions that can be applied to a wide range of issues. Results ------- As discussed in the previous sections, one possible solution to the challenges of narrow-minded special interests, uninformed voters, and politicians with hidden agendas in public policy decision-making is to use a framework that systematically evaluates policy decisions. Specifically, we propose a framework using large language models (LLMs) that can analyze policy documents and identify potential consequences, both intended and unintended, of policy decisions. During the development of this framework, we started with the assumption that an LLM could be used to analyze policy documents and identify potential consequences. We then considered several alternatives and scenarios for how this could be done, including the use of LLMs to analyze voter guides or to write the text of voter guides. However, as we explored these alternatives, it became clear that what we were actually doing was trying to figure out a general purpose framework that could be used to evaluate policy decisions from multiple perspectives. Through our collaboration with Mistral AI, we developed a framework that uses three dimensions to evaluate policy decisions: likelihood, magnitude, and relevance. The likelihood dimension refers to the probability that a given consequence will occur, while the magnitude dimension refers to the size or significance of the consequence. The relevance dimension refers to the importance or significance of the consequence to the stakeholders involved. The interactive co-authoring process was critical to the development of this framework. By working together, we were able to identify potential consequences that might have been overlooked by a single author or analyst. We also were able to test and refine the framework through a series of iterative discussions and analyses. For example, we initially considered using only the likelihood and magnitude dimensions to evaluate policy decisions. However, through our discussions, we realized that it was important to also consider the relevance of potential consequences to stakeholders. This third dimension allowed us to better account for the diverse perspectives and interests of different groups. We also considered how the framework could be used to evaluate different types of policies, from broad macro-scale policies to more specific micro-scale policies. We found that the framework was flexible enough to be applied to a wide range of policy issues, from healthcare to transportation to education. Once the potential consequences and trade-offs were identified for each policy scenario, the LLM was used to identify potential solutions for optimizing outcomes. Specifically, the LLM was prompted to generate a list of possible interventions or policy adjustments that could mitigate negative consequences and enhance positive ones, while taking into account the interests of different stakeholder groups. These potential solutions were then evaluated using the same likelihood, magnitude, and relevance criteria used to assess the consequences and trade-offs. The framework provides a systematic and transparent approach to evaluating policy decisions. By considering the likelihood, magnitude, and relevance of potential consequences, decision-makers can make more informed choices that take into account the full range of potential impacts and trade-offs. By using an LLM to analyze policy documents, the framework can also help to minimize latent biases and expand perspectives, leading to more equitable and effective policy outcomes. Case Studies ------------ To demonstrate the effectiveness of the proposed framework, we applied it to a range of policy issues, from local level initiatives to macro-scale challenges. Initially, we used Proposition 23 as a case study to demonstrate the application of the framework to a specific policy issue. Proposition 23 was a proposed ballot initiative in California that aimed to regulate dialysis clinics by requiring them to have a physician on-site during treatment hours. As we refined the framework, we expanded the scope of our analysis to include broader policy issues. One such issue was electric vehicle (EV) subsidies. Finally, we applied the framework to the issue of business improvement district (BID) taxing. BIDs are special assessment districts that levy taxes on businesses within a specific geographic area to fund improvements to the district. Recognizing the potential for the framework to be applied to larger policy challenges, we bifurcated the framework at a higher level of dimensionality to address macro-scale issues. Specifically, we identified two broad categories of policy challenges: those related to climate change and those related to economic inequality. For each of these categories, we applied the framework to identify potential consequences and trade-offs from multiple stakeholder perspectives. For all policies we used the framework to evaluate the potential consequences and trade-offs of various policy interventions and considered the impact of these policies on different stakeholder groups. It’s important to recognize that different types of policies will have different consequences for different groups of stakeholders. Put another way: what may happen, and to whom. One of the key advantages of using a large language model (LLM) for policy analysis is its ability to simulate the potential effects of a policy based on synthesis of its training data. This means that even if the specific details of a policy are not known, the LLM can make well-founded assessments of its potential impact based on its knowledge of similar policies and their outcomes. Discussion ---------- **Policy Simulation using LLM Training Data** For example, in the case of the business improvement district (BID) taxing case study, the LLM was not explicitly trained on BID taxing policies, but it was able to synthesize information from its training data on related policies, such as property taxes and special assessment districts. This allowed it to make informed predictions about the potential consequences and trade-offs of a BID taxing policy. Similarly, in the case of the electric vehicle (EV) subsidy policy, the LLM was not explicitly trained on the specific details of the policy, but it was able to draw on its knowledge of related policies, such as renewable energy subsidies and carbon pricing, to make informed predictions about the potential impact of the policy. This ability to simulate policy effects based on synthesis of training data is an incredibly powerful tool, as it allows policymakers to evaluate the potential consequences and trade-offs of a policy before it is implemented. This can help to reduce the risk of unintended consequences and ensure that policies are designed to achieve their intended goals in the most effective and equitable way possible. It’s worth noting that while LLMs can provide valuable insights into policy effects based on their training data, they are not a substitute for expert judgment or on-the-ground knowledge. Policymakers should always consult with relevant stakeholders and experts when designing and implementing policies. However, LLMs can be a valuable tool for supplementing expert judgment and identifying potential consequences and trade-offs that might otherwise be overlooked. Limitations