Negotiations with Blockchain and IoT

For decades, humans have imagined a world where machines take over some human functions. In the past decade alone, we have seen rapid strides in technologies such as artificial intelligence (AI), the Internet of Things (IoT), and blockchain. Soon, we may see a significant shift in machine-led decision-making and negotiations.

Imagine a world where your own AI agent can negotiate your next vacation costs. Without having to attend a negotiation class, the agent may even bargain lower mortgage rates with your bank. Here are four possible scenarios of how IoT and blockchain may work together to enable autonomous negotiation.

Smart Devices in Decision-Making

According to Statista, at least 3.2 billion people in the world have a smartphone. In addition, an increasing number of people use smart devices. Alexa, Cortana, and Google Home are examples of virtual assistants created to make our lives easier.

These devices can monitor, track, and share information about our daily habits. IoT is a system of interconnected devices that "talk" to the cloud. The devices collect and process data, and decide an action based on the data. The IoT has a growing database on where we eat, who we talk to, what we buy, and what we spend money on. IoT can use this ever-growing database to build an accurate model of a user's preferences.

Integrating blockchain technology with IoT can lead to higher decision-making functions. Blockchain technology promises a free global market, secure from manipulation. Blockchain can integrate digital certificates and smart contracts to create autonomous agents. Using your IoT-gathered preferences, these agents can act on your behalf, fully informed.

Imagine a futuristic supermarket, such as Amazon Go, using deep learning to buy your groceries and deliver to your doorstep automatically. The supermarket would consider your habits to make decisions about your shopping list. For example:

  • How many times you take coffee per day
  • How much milk you use
  • Your sugar consumption
The supermarket would use blockchain's seamless point-of-sale (POS) integration to process your payments as you accept the deliveries securely. 

Autonomous Agents in Game Theory

Autonomous Agents
For most humans, a negotiations class and continuous practice can be necessary to learn and improve negotiation skills. For autonomous agents, the use of algorithms can lead to instant win-win solutions.

Game theory is the mathematical construct guiding strategic interactions between rational decision makers. Game theory means autonomous agents will be bargaining amongst themselves to present you with rational options.

Picture this. Your neighbor has a new baby and wants to upgrade to a bigger car. You are single and new in town, and you need a car. Your autonomous agent "talks” to your neighbor's autonomous agent. The two agents would consider factors like:

  • Depreciation on the neighbor's old car
  • Your options with other sellers
  • Your neighbor's options with other buyers
  • Your earnings
  • The length of your commute
  • The size of your parking space

In an instant, your agent presents you and your neighbor with an ideal win-win proposal. That's how easy future negotiations could become.

Negotiation Analysis and Autonomous Decision-Making

In a negotiations class, humans learn to use people’s responses to adjust their offers. Autonomous agents analyze the actions of others to recommend the best user action. For example, the way chess programs make decisions based on the player’s moves.

For autonomous agents, negotiation analysis is a step up from game theory. In game theory, the agent analyzes actions already committed. In negotiation analysis, agents examine both actions committed and the actions that may result from the user's decision. The autonomous agent creates a make-believe negotiation simulation in order to calculate the best outcome. The difference in the two approaches is that in negotiation analysis, the agent can execute decisions with complete authority, without user intervention.

For example, we already see connected and autonomous vehicles (CAV) or driverless cars, which make route decisions independent of user input. The passenger may choose the destination, but the car maps out the route depending on traffic, weather, and speed limits.

Machine Learning in Nonstationary Preferences

Humans change their minds and vary their preferences over time. For instance, your needs and behaviors while in college differ from your needs five years into your career. As your tastes and preferences evolve, so does the value you attach to particular items.

Autonomous agents may, in the future, have the capacity to vary values according to the user's change in preferences. The main challenge to IoT tracking nonstationary preferences is that users are not yet comfortable opening up their privacy to machines.

The public is wary of privacy and security concerns such as fear of government monitoring, big corporates meddling, and criminal stalking. As online security matures, so is the expectation that IoT adjustment to nonstationary preferences will evolve. 

The Role of Autonomous Agents in Negotiations

The integration of IoT and blockchain technologies can lead to autonomous agents providing optimized outcomes in many interactive scenarios. IoT may later be able to track and even predict user preferences accurately. Blockchain can provide secure integration with POS systems for fast negotiations and cashier-less checkouts.

Autonomous agents will likely operate on game theory, negotiation analysis, and preference elicitation to present users with win-win agreements on everything from shopping, transport, lifestyle, and most other areas of human interaction.