Building the ecology of the Metaverse
This post is about an area cluster of interest to a group of OMI members. It does not represent all OMI members, or all of OMI's interests.
We are a collective, using digital technologies to address the challenge of interacting with complex knowledge ecosystems. We use people-centric technology development to empower informed, accountable action from the bottom up. Using digital hyper-connected, integrated technologies - what we understand as the Metaverse - potentially gives grassroots initiatives the ability to contribute to the growing integrated knowledge ecosystem - the Metaverse as an ecosystem - and thereby scale their efforts and networks globally. We are looking for consortium members to pursue research funding, or strategic partnerships with companies interested in the commercial potential of these approaches. Please find some use cases below with more information. We also welcome anyone to join the journey; find us on Discord - the Open Metaverse Interoperability Group (OMI). Interested? Email admin@omigroup.org
Common needs observed
There is a lot of information out there. However, there is a lack of globally accessible user-friendly resources for outsiders, like visitors, educators and researchers to access this information in a way that makes sense. The complex information available about our interactions with nature, for example in biosphere reserves, or our interactions with natural resources, for example in water resource management, including key characteristics, infrastructure, and services is virtually invisible to anyone outside of those directly working with the data. What's worse, as outsiders it is hard to even find out how to get help finding the information!
The lack of integrated information about complex environments, e.g. biosphere reserves or urban resource management, hampers research on human usage patterns and experiential impacts on conservation and local development, resulting in a noticeable gap in studies on user perceptions and behaviours, particularly concerning digital tool adoption and trade-off evaluations.
Opportunity presented
A unified knowledge infrastructure, which may include a platform with searchable data, interactive maps, and clear support channels would greatly improve the discovery and exploration experience.
Further, having an open, integrated, user-friendly knowledge infrastructure like this has great potential in for example education, or even extending the game development ecosystem to include entertainment games that incorporate physical world assets.
Technologies used
System-of-systems data integration
The approach of systems-of-systems integration of data sources (e.g., environmental, terrestrial, meteorological, and marine) at multiple levels (i.e., syntax, semantics, or conceptual), including those in different sectors e.g. the private sector or government allows a more human-centric approach to data integration; meeting people where they are rather than trying to get everyone to agree on a new approach or standard.
Rather than integrating each system on a one to one basis, this approach achieves interoperability between different data sources through an Evolutionary Architecture described by IPSME (Nevelsteen & Wehlou, 2021). Instead of only gathering diverse stakeholders around a table in an attempt to reach a common approach to data management, IPSME is conducive to rapid prototyping and iterative development. In the Evolutionary Architecture, stakeholders can be added and removed dynamically to the ecosystem, in runtime, and with no downtime for the systems being integrated.
Rather than re-engineering existing data sources, IPSME supports legacy systems i.e., where the technical knowledge of the system as been lost or the risk of introducing errors in long running systems would be detrimental.
AI knowledge assist modules
Artificial Intelligence (AI) will be employed to implement interoperability in IPSME and assist in analysing and manipulating data, external to the systems being integrated for pattern recognition in decision making or different visualizations of the data. Machine learning models, colloquially known as “cooperative AI” can assist decision-making in complex systems (Wang et.al., 2024).
The function of the language model-based AI is to serve as a knowledge base and reduce misunderstanding among stakeholders. It is useful especially when complex tasks need to be decomposed into smaller pieces. Multi-agent coordination algorithms can then be employed to provide comprehensive model outputs.
Several pilot sites are crucial to provide access of data, information, infrastructure, tools, etc. Google’s NotebookLM has become a prevailing tool to build local knowledge base for each pilot. It works same as RAG, but more powerful and intelligent when handling multi-source data.
Deep learning and machine learning, as well as wider areas of AI applications (chat, wearable devices, computer vision), AI policies, governance models, and AI-driven human-nature interaction methods is also of interest.
Data visualisation / extended reality visualisations
Visualisations have two categories, framework and platform. A "framework" is a toolbox for developers to build things. Members in our collective are developing new frameworks to be more accessible to a wider range of users, be able to do different things, and be more easily interoperable.
A "platform" is a finished place for users to do things. Frameworks are used to build a platform, and some framework builders have their own platform as a "reference implementation". The apps and websites we are most familiar with - Open Simulator, SecondLife, VRChat, Facebook ... are all platforms. Implementing solutions on existing platforms is currently contracted out to companies external to the collective.
Knowledge with agency
Applying so-called Metaverse methods and protocols, including systems of systems data engineering, AI knowledge assist modules, and extended reality visualisations to integrate diverse knowledge infrastructures through a people-centric lens, enables the development of technology that encourages individuals and groups to engage critically with information.
Use cases
AquaSavvy: Participatory Urban Water Management
https://aquasavvy.eu/ Bringing water sensitive urban design into digital twins of cities, creating a visual canvas tool through and for participatory planning perspectives. This is the first funded prototype of our approach.
Biosphere Metaverse: Contribute research in human-nature interactions facilitated by digitalized knowledge system
Current proposal in development for the NFRFI2026 funding call.
1) It is difficult for visitors (especially international visitors) and researchers to find integrated information about each biosphere reserve (type, size, climate, zoning, accessibility, etc.). They also don’t know where they can get help for information acquisition. Basic infrastructure of databases, search tools and interactive maps that should allow different users to easily find and explore, e.g. type of ecosystems, zoning, accessibility, public transport, activities, local services, etc.
2) Consequently, there seems to be limited research on how people actually use and experience biosphere reserves, and how this affects conservation and local development. Much work on biosphere reserves focuses on ecology, land use or governance structures, but less on everyday practices and lived experiences of users. There are relatively few systematic studies on how different groups perceive trade-offs between conservation and development, and how digital tools shape access and engagement.
3) Given the current state of technology, there is considerable potential for AI-supported tools. For example, systems that help visitors plan trips based on their needs and constraints, multilingual conversational interfaces that explain rules and ecological values, and analytical tools that combine ecological and social data to better understand socio-ecological dynamics.
Building a cybernetic library network
Weaving the knowledge commons
in draft, currently included in the biosphere project as a complementary use case.
Education as an Emergent game, where Metagaming is core to play
"Exploring the world is great and all that, but poking fun at exploring the world? Oh hell yes." - Mike Sowden
What is an emergent game?
Emergent games refer to a type of gameplay that arises from the interactions and behaviors of players within a game system, rather than being predetermined by the game designers. This type of gameplay is often characterized by complex, dynamic systems and open-ended rulesets that allow players to create their own experiences and stories within the game world. The sandbox concept is one application of this.
Applying the emergent game context to education can create an ecosystem where creativity can thrive, for the learners as well as the educators. This approach shifts the focus to the player, focusing on building a playground with relatively open goals, and the verbs and game mechanics to find their own fun and solutions to the problems presented. As the ample literature on using Minecraft for education shows, this is a very promising area for education, but it is still often a limiting and frustrating experience.
We can improve on this in one way by extending the ecosystem beyond a specific platform. That is part of our interest in the Metaverse, and the focus of the interoperability aspect. If you choose to have your community, and your virtual education, for example in Second Life, and you want to join up with another community in Open Simulator, you should be able to do that. And in Roblox, or Minecraft, or wherever you want. Perhaps you have a unique platform developed that is specific to your needs, or someone made an industrial simulation available for education, those should seamlessly interoperate into your activities or curriculum.
What is Metagaming in this context?
In traditional role-playing games, metagaming refers to the use of real-world information that the player character should not know about. A metagame can also refer to achievement systems and other official elements outside the actual core game. While the use of the term varies by context, the different meanings have in common a reference to something beyond the game itself. In our modern world where Wikipedia was first scoffed at as a resource, then reluctantly embraced, and with the current challenges and opportunities with AI, education needs to move from ring-fencing the "game" of education, to teaching how to engage critically with the wealth of information (and misinformation) that exists beyond the education sandbox.
In their article “A Typology of Metagamers: Identifying Player Types Based on Beyond the Game Activities”, Kahila’s group investigated how school learners engaged with games for educational purposes, and they looked at what happens beyond the game. They talk about three distinct profiles of players: versatile metagamers, strategizers, and casual metagamers, and maps their metagame activities into the main categories of game-enabling activities, strategizing activities, discussing activities, information-seeking activities, creating and sharing activities, and consuming activities.
Moving Education to be responsive to modern needs.
The advances of structuring data in for example geospatial mapping and knowledge infrastructures more generally unlocks potential for using physical world assets in games. Using physical world assets in emergent approaches to game design is well suited to allowing players to interact with their game worlds in varied ways. Exploring playing with the physical world – morphing and changing it – through games can allow us to learn about the world not through a top-down education, but through a curiosity that does not even have to involve the truth. Through these games we can build a new sense of belonging, that builds a common language across polarised opinions, because it’s just for fun, after all.
Including physical world assets into entertainment games - play for play's sake
in draft
Transformative management
in draft