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| # <p style="text-align:center"> M&A Negotiation Simulator: Documentation </p> | |
| ## Background and Context | |
| One of the complexities of the legal industry is that much of the work and value-add is implicit. It is measured by experience and the specific know-how of the client and industry-base. We argue that the greatest value-add for legal practitioners is not captured in the explicit knowledge (e.g. case law, contracts, legislation and regulation, etc.). Rather, it is the latent methodology behind information synthesis, issue-spotting, judgment and eloquence of argumentation that makes the quality of legal work highly diverse and varied. | |
| Accordingly, how legal professionals describe the nuances of their practice, capture and abstract their lessons from experience (i.e. wisdom) distinguish and set apart the next generation of legal experts. Our motivation is to elevate the starting skills of all lawyers, equipping future and current practitioners with tools that will continuously harness and perfect their craft. | |
| In collaboration with Flatiron Law Group, we have worked towards developing the first platform that would allow legal professionals to “pilot” (i.e., simulate) negotiations at varying starting positions, extent of buyer/seller leverage, complexity of legal issue at play, and the impact on the deal. An important note is that this platform is focused on negotiation prior to the signing of a Letter of Intent and Term Sheet. This means that we train the model on the premise of tacit expertise and not on any confidential documents. The goal here is to shift away from legal work product and towards legal work process. | |
| ## How to Use: Flow of the Negotiation Game | |
| The sample module that is openly available for experiment consists of an equity sale between a fictional buyer and seller. The buyer is interested in a seller that has a proprietary SaaS solution for small, medium-sized business retailers. The fictional buyer believes this acquisition could help extend its market presence and technological capabilities. The lawyer-user is representing the buyer. The AI agent (robo-counsel) is representing the seller. | |
| As the lawyer-user, the first task is to enter the name and select the practice level (i.e., junior, senior, or partner). Depending on the level selected, the fact patterns will adjust accordingly. So, if a lawyer-user selected Junior, the “main focus” will be “Warranties and Representations”. If a lawyer-user selected Senior, the “main focus” will continue to be Representations and Warranties but will add a new risk factor of a “Cybersecurity Incident,” and will include a description of those new facts. | |
| The broader fact pattern of the equity sale remains consistent across the Junior, Senior, and Partner level. New facts are added to the original fact pattern depending on the level of difficulty. This is intentional and consistent with practice whereby the complexity of the simulation mirrors the impact to the deal. While senior associates could help manage slightly more complicated incidents that arise and have an effect on the deal, partners are immediately involved when situations get nuclear. | |
| Depending on the level selected, there may be sample prompts available and/or hints. A lawyer-user may click on the button for a “hint” in the event they get stuck or are struggling with where to start. Once the onboarding is complete, the lawyer-user is now ready to start negotiating with the agent (robo-counsel). | |
| Finally, when the lawyer-user has determined that it has reached a strong deal in representation of the buyer, the negotiation will conclude, and the chat dialogue may be saved. The dialogue can be downloaded and shared as a document to the lawyer-user’s mentoring partner and other colleagues for internal training purposes. | |
| ## Model Architecture | |
| The platform applies a traditional agentic model known as BDI (otherwise, belief-desire-intention). The idea behind this framework is to ensure that the model learns to think slowly, walking through and enumerating the specific strategy prior to generating a response. | |
| The model uses prompt engineering and is not fine-tuned on any domain-specific data. This means that the system prompt, how that is written, is key to the success and knowledge curation of the tool. The system prompt provides AI agent with its knowledge base, a detailed background on the specific subject matter related to the broader fact pattern for the negotiation. In the case of the sample module, this would be key definitions and relevant concepts related to the purpose of representation and warranties and how they relate to other risk (i.e. liability) mitigation tools such as indemnification obligations. Importantly, the BDI is appended to the system prompt to allow the model to not only draw on its knowledge base, but also to understand how to react to the lawyer-user in the negotiation. | |
| ## Intended Use | |
| The platform is used for educational and research purposes only. We encourage you to experiment with our sample module and consider building your own, tailored to the specific legal domain that may be of interest. If you have any additional questions or are interested in contributing content and features, please reach out to the developers at their respective institutional email addresses. | |
| ### Platform developers | |
| Hasan Abed Al Kader Hammoud, Hani Itani, Megan Ma, Leonard Nuara, Conrad Everhard | |
| ### Release Date | |
| August 1, 2024. Alpha prototype originally demo-ed at CodeX FutureLaw on April 11, 2024. | |
| ### License | |
| MIT or Apache-2 License |