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Application Areas

Environments

AEAs are most suited for environments which are:

  • Decentralized: there isn't a central authority that controls, manages, or makes decisions.
  • Multi-Stakeholder: the domain, problem, or solutions involve multiple distinct stakeholders.
  • Peer-to-Peer: interactions are (or could be made) direct and peer-to-peer.
  • Complex, Incomplete, and Uncertain: to the point that off-loading tasks to computational entities becomes valuable.

Applications

We identify a number of application areas for AEA-based solutions. This list is by no means comprehensive. In fact, we are most excited about applications which we have not thought of before.

Automation

AEAs can automate well-defined processes in different domains, such as supply chain, mobility, finance, ...

Micro-transactions

AEAs make it economically viable to execute trade involving small values. An example is use-cases with many small sellers (e.g. of data) on the supply side.

Wallet

AEAs can simplify interactions with blockchains. By acting as "smart wallets", they can hide away the majority of the complexities involved in using blockchains for end users.

IoT

Agents representing objects in the IoT (Internet of Things) space. For example, AEAs paired with hardware devices such as drones, laptops, heat sensors, etc., providing control and receiving data from the device. An example is a thermometer agent.

Web 2.0 <--> Web 3.0 interface

Agents that interface and bridge the gap between existing (Web 2.0) and new (Web 3.0) economic models. An example is an AEA that communicates with HTTP clients/servers.

Digital data sales

Agents with access to some data sources that sell the data, access to the data, or access to the usage of the data. An example is an AEA that continuously sells data to another AEA, who in turn uses it to improve their reinforcement learning model.

Multi-Agent System VS Agent-Based Modelling

The AEA framework enables the creation of multi-agent systems as technological solutions to real world problems.

Although there are some overlap, the framework is not designed from the outset as an agent-based modelling software, where the goal is scientific behavioural observation rather than practical economic gain.

Moreover, there is no restriction to multi; single-agent applications are also supported.