Robust Mechanism Design for Smart Equipment RWA Market / Robonomics Network

Recent advancements in machine learning (ML) have opened up a massive influx of opportunities for robotics to automate new tasks in the real world. While industrial robotics remains the dominant segment by size, service robots - those that operate among people - have seen the most rapid growth. The estimated dollar value of the professional service robotics market was approximately USD 21.6 billion in 2023, with a 38% compound annual growth rate. Breakthroughs in ML and the growing labor shortage have created a unique moment where service robotics is finally poised for widespread implementation, addressing critical needs across industries and in our daily lives.

“Small and medium-sized enterprises (SMEs) need easy access to automation” and “Democratizing” robotics are the key trends in robotics now, according to IFR. Logistics and warehousing robots have been leading this trend. First deployments of city delivery rovers have started and it is clear that route optimization is critical to keep the margins. Medical robotics have seen a huge upsurge during the pandemic and have also been one of the leading categories since, with disinfection and sterilization being the dominant use cases. Hospitality rovers in hotels and restaurants have seen a big spike in Asian markets already and we start seeing them more in Europe and the US. Agriculture, professional cleaning, inspections and maintenance, construction and heavy industries, security / search and rescue - the list of new application areas keeps growing. Some of the most optimistic predictions say that we may have more humanoid robots than people in the next decade.

Here is the list of Top-5 application areas for professional service robotics in 2023, according to the International Federation of Robotics:

However, as these technologies evolve, traditional financing approaches fail to keep up with the unique challenges posed by smart equipment leasing and investment in robotics.

Traditional models, like loans or leasing, are often inadequate for smart equipment due to rapid technological obsolescence, information asymmetry, and performance uncertainty. Robotics and AI technologies evolve quickly, making long-term investments risky, as traditional models do not account for the frequent upgrades or retrofits required. Manufacturers often have more knowledge about equipment performance than lessees, leading to inefficiencies in capital allocation or mispricing. Additionally, fixed payment schedules fail to reflect real equipment value, especially when performance depends on software updates or integration. The lack of expertise among financial institutions in managing AI-driven assets further contributes to underinvestment in these high-potential technologies.

A better capital allocation system is required for smart, connected equipment. One that will be responsive to the much faster pace of innovation and resilient to uncertainties and asymmetric information held by different market participants.

Principles of Robust Mechanism Design

Mechanism design is a field in economics and game theory that focuses on designing rules or systems (mechanisms) to achieve specific objectives, even when participants have private information and may act strategically or collude. A classic example is designing auctions where bidders have private valuations of the goods being auctioned.

A key challenge in robust mechanism design is handling type spaces—all possible characteristics of agents, like their preferences and beliefs. Since we can't account for all possible type spaces, mechanisms must be designed to handle diverse situations where agents may not fully know each other's preferences or beliefs.

There are many different agents that can be directly involved in equipment deal, like equipment manufacturers (OEMs), integrators and maintenance providers, asset originators, underwriters, and investors. Coordination among them involves significant transactional costs, not to mention the additional complexities of addressing broader public interests. Despite the complexity of these interactions, we have a goal to develop a robust mechanism that:

Ensures incentive compatibility for all market participants
Allocates capital to achieve ex-post efficiency
Remains resilient to uncertainties in market conditions and the beliefs of participants.

Let me illustrate this with a simple example to make it easier to understand:

Incentive Compatibility: Imagine you're playing a game where you need to tell how much you like different candies. If you tell the truth, you'll get your favorite candy. Incentive compatibility means that your best option is to be honest because that way, you'll get what you really want. In other words, being truthful is the smartest move.

Ex-Post Efficiency: After everyone have shared their favorites, the candies are handed out. Ex-post efficiency means that everyone ends up with the candy they like best, and no candy goes to waste or is given to someone who doesn't like it. It ensures that everyone gets their favorite treat, making everyone as happy as possible with what they receive.

I believe it is important to use incentive compatible mechanisms when introducing such an important new technologies, like Robotics and Embodied AI, that have the potential to fundamentally alter the fabric of society. Ensuring that these mechanisms are robust is essential for maximizing social benefit and achieving fair, efficient outcomes in the deployment of transformative technologies.

Real-World Asset (RWA) Market Design for Smart Equipment

Designing a robust mechanism for capital allocation into new categories of smart equipment is incredibly challenging due to the complexity and evolving nature of the field. However, I see an opportunity for several strategies to enhance robustness of this mechanism.

Digital Twins and Beyond

This is at the core of what Robonomics does. In the article "Introduction to Digital Twins", we explained the concept - digital representations of physical entities or processes. Digital Twins are not static models but dynamic systems that continuously receive real-time data from their physical counterparts, allowing for ongoing updates and accurate reflections of the current state. This continuous data flow enables precise simulations and analyses, facilitating informed decisions regarding the operation, maintenance, and optimization of physical assets.

To effectively capture and manage data from smart equipment, Robonomics offers an open-source tool called Otaberu. Otaberu is designed to facilitate peer-to-peer (P2P) networking for edge devices and robotics, enabling seamless and secure communication between devices without extensive network configuration. It provides a robust framework for data collection and management of digital twins.

Digital twins serve as the foundational layer for implementing robust mechanism designs for capital allocation in smart equipment markets. By providing a comprehensive and real-time digital representation of physical assets, digital twins enable precise monitoring and analysis of equipment performance, usage patterns, and maintenance needs. This detailed insight allows for the development of sophisticated strategies that optimize capital allocation, ensuring resources are directed efficiently to maximize returns and enhance the longevity and productivity of smart equipment.

Digital Twins in the context of capital allocation also open the conversation about autonomous robocorporations, which are guided by on-chain governance mechanisms. These autonomous entities can independently execute decisions based on market input, making them capable of managing assets, allocating resources, and even engaging in economic transactions without human intervention.

Prediction Markets

Prediction markets and robust mechanism design both aim to elicit truthful information from agents with private knowledge to achieve efficient outcomes. By integrating prediction markets into our mechanism framework, we can improve ex-post efficiency of capital allocation decisions by ensuring that decisions reflect the most up-to-date and accurate information available, leading to better alignment of resource allocation with realized outcomes.

The following steps illustrate how the process could be implemented:

Establish a prediction market for each category of smart equipment under consideration (delivery rovers, packaging arms, etc.).
Allow a broad group of stakeholders to trade contracts based on future performance metrics of the equipment, such as uptime percentage and maintenance costs.
Use the market price to reflect the collective belief about whether the equipment will meet specific performance benchmarks over the lease period.
If the prediction market indicates a high probability that the equipment will perform well (e.g., high uptime and low maintenance costs), allocate more capital towards leasing that equipment.
Conversely, if the prediction market suggests poor performance, reduce capital allocation or adjust lease terms to mitigate potential losses.
Prediction market participants are rewarded or slashed depending on the bets they make.

Prediction markets allow for efficient aggregation of private knowledge from diverse stakeholders, real-time updates on performance expectations, and continuous adaptation to new information, which reduce uncertainty and result in a more resilient mechanism design.

When new information becomes available, such as updated Digital Twin of the smart equipment, prediction market prices adjust accordingly, allowing for dynamic reallocation of capital. This helps mitigate the risk of over-investing in underperforming assets while ensuring that high-performing assets receive appropriate funding in the long run.

By aligning capital allocation with continuously updated market insights, underwriters can make better-informed decisions, optimize their portfolios and reduce financial risk.

Risk-sharing strategies play a crucial role in reshaping capital allocation mechanism for smart equipment, helping to balance risks and increase incentive compatibility. Traditional leasing arrangements are relatively rigid, typically relying on fixed payments without accounting for the equipment's dynamic performance or unexpected changes.

Strategies like insurance-style contracts or performance-based bonuses can be employed to create a more flexible and adaptive approach that effectively shares risks and lead to a more robust mechanism design.

For instance, an insurance-style contract can help distribute the risk of unforeseen downtimes: if Digital Twin data reveals unexpected downtime due to manufacturer defects, part of the payment might be refunded. This way, the financial burden of operational issues is mitigated, and everyone is incentivized to maintain performance.

The pay-for-outcomes model provides an additional layer of risk sharing. Robots-as-a-service (RAAS) model is gaining momentum in robotics, but is still considered novel by many traditional equipment financing players. By tying payments to the business benefits generated by the technology (like the number of boxes moved in a day or miles driven in autonomous delivery) participants are encouraged to focus on achieving tangible outcomes.

For example, if a logistics robot enhances warehouse efficiency by 20%, a portion of that efficiency gain can be used to fund the monthly payment. This approach helps businesses connect the cost of equipment to their own operational success, reducing financial risk during uncertain periods when outcomes might be less predictable. The alignment here is twofold: businesses feel more secure that they are only paying in proportion to the value they receive, while suppliers are encouraged to provide equipment that reliably delivers business benefits.

With smart equipment that can generate data in real-time, robust risk-sharing mechanisms become feasible, allowing for dynamic responses to performance issues and aligning incentives around actual outcomes. This evolution in leasing structures allows market participants to respond adaptively to technological change, ensuring that the risks and rewards of adopting advanced equipment are shared more equitably. In effect, the relationship becomes more of a partnership, rather than a rigid contractual arrangement, fostering trust and encouraging innovation.

DAO Governance for Equipment Origination and Underwriting

It is crucial to establish governance structures that support efficient, incentive-aligned capital allocation over the next 3-5 years as the robotics industry takes shape.

Decentralized Autonomous Organization (DAO) is an ideal governance structure for managing the underwriting process in smart equipment deals. It’s main goal is to determine which new categories to support and how to allocate capital effectively.

By applying principles from robust mechanism design we can improve the odds that resources are directed towards high-value investment opportunities while minimizing risks associated with asymmetric information and market uncertainties.

The Underwriters DAO should use auction-based mechanisms to make informed bets on promising categories. Prediction market insights should enhance the accuracy of valuations and inform bidding strategies here. An autonomous process can then allocate capital optimally, based on the unique combination of beliefs held by the members of the Underwriters DAO.

As the initial capital is pooled in the DAO and allocated, a feedback loop based on Digital Twin data starts to drive further decision-making. At this stage, a larger pool of investors can start participating via additional tranches, offering hedge, equipment-backed product to digital asset investors.

The ability to decide which deals are offered to the larger community of pooled investors is critical, and successful bets should be appropriately rewarded. This is what creates the value for being a part of the governance of the DAO on its own, separate from the asset-backed stake proportionate to capital invested into smart equipment.

Here is a simple scheme of operation for such a DAO:

Practical Considerations and Next Steps

This is just the beginning. My goal is to create a robust capital allocation mechanism that makes the productivity gains of the robotics revolution accessible to everyone.

Ideas above offer a good stating point, but there are limitations that need to be considered. Participants may still engage in strategic behavior if they believe they can influence prices for personal gain and limited liquidity at the early stages can make it even easier. With enough liquidity in prediction and capital markets this can be overcome, but at the early stages it is important to carefully pick the members of the Underwriters DAO.

Steps to launch robust mechanism for capital allocation in new categories of smart equipment:

Launch prediction markets to choose new categories
Form Underwriters DAO to start making first deals
Use risk-sharing contracts to manage portfolio risk
Use Digital Twin Data to improve allocation further
Develop a structure for broader investor participation in the robotics revolution productivity gains

If you're interested in being a part of this journey or just want to ask a few questions - leave you email below.

Vitaly, Evangelist of Robonomics

Robust Mechanism Design for Smart Equipment RWA Market