Role of Communication, Data Analytics and Artificial Intelligence in Resource Allocation
The network resource allocation problem considered in Chapter 2 and the mechanism design framework discussed in Chapter 6 have several features in common. They comprise of a set of players which have preferences over their outcomes known privately to them. In the network resource allocation problem, the outcome for a player is the amount of resource al- located to that player, for example, bandwidth over the Internet. Resource allocation more generally would include things such as transportation units in vehicle routing or delivery systems, servers in computation networks, advertising space or visibility in social networks, or contract provisioning in financial networks or labor markets. On the other hand, player outcomes could take various forms as the delay experienced by a driver or a customer receiv- ing a delivery, the quality of the goods or services provided to the users, or it could be the financial gains or prospects associated with the outcome. The system operator is primarily responsible for allocating resources. In the network resource allocation problem, the system operator is assumed to allocate resources to each player provided they satisfy the capacity constraints. But more generally, as considered in the mechanism design framework (Chap- ter 6), it implements an allocation from the set of available allocations and the implemented allocation in turn influences the outcomes for each player.
The system operator is central to this setup to facilitate optimal resource allocation. It communicates with each player, sending messages that provide important system related information to the player which affect the players beliefs and actions. These messages could take the form of providing available options to the players, their corresponding prices, and the uncertainty associated with different outcomes. For example, a ride hailing platform provides the riders with different traveling options with estimates about their delays, service experience, and associated prices. The players respond to these messages through appro- priate signaling channels provided by the system operator. These responses are governed by the players’ private information about their types and their surroundings. For example, in the ride hailing example, their flexibility with time of arrival or departure, their budget, their knowledge about the people around them seeking transportation services, etc. Their responses are also affected by the behavioral traits displayed by them and the players around them and as well as their strategic policies. The system operator aggregates all these signals from the players and then allocates resources accordingly. Additionally, the system operator also maintains information about the environment and in conjunction with the information collected from the players, it is able to allocate resource more efficiently. It is evident that for the proper functioning of a system under this setup is closely tied to the communication protocols provided by the system operator and its ability to learn from them.
Notice that we are focusing our attention over markets where there is a central planner (or what we are calling a system operator) who is deciding the underlying mechanics of the system within the physical constraints and providing communication channels to the players to indicate their individual needs and preferences. Although the communication protocol often leads to a decentralized market-based mechanism, the freedom of choice for the users is restricted to the options provided by the system operator, and it is important that we maintain caution in designing these protocols. Having a central planner definitely brings the perks of improved efficiency due to potentially better planning opportunities but this is conditioned on the availability of enough relevant information. Hayek argues:
If we can agree that the economic problem of society is mainly one of rapid adaptation to changes in the particular circumstances of time and place, it would seem to follow that the ultimate decisions must be left to the people who are familiar with these circumstances, who know directly of the relevant changes and of the resources immediately available to meet them. We cannot expect that this problem will be solved by first communicating all this knowledge to a central board which, after integrating all knowledge, issues its orders. We must solve it by some form of decentralization. But this answers only part of our problem. We need decentralization because only thus can we ensure that the knowledge of the particular circumstances of time and place will be promptly used. But the “man on the spot” cannot decide solely on the basis of his limited but intimate knowledge of the facts of his immediate surroundings. There still remains the problem of communicating to him such further information as he needs to fit his decisions into the whole pattern of changes of the larger economic system.
In the years following the period Hayek made this remark, communication technologies have made huge progress. Data collection and signaling delays have gotten reduced to microseconds with the advent of the Internet, and indeed we are seeing a lot of centralized markets in the form of big online marketplaces. But there are still several bottlenecks showing up in the form of learning useful information from all the signals and data. Besides, incorporating behavioral features in the design of these systems in a principled manner can go a long way. For example, we saw in Chapter 6, that a mediated mechanism which includes a messaging stage from the system provider to the players can help implement social choice functions which are not implementable otherwise (plus implement them truthfully by direct mechanisms). More practically, this would transform into artificial indicators from the system provider to the players that help align the players beliefs. For example, consider a food delivery platform that allows customers to order food from restaurants through their app. The platform can initiate a program where they randomly select some users and provide them with discounted service for long distance orders. As the players use this app repeatedly, they start modifying their strategies in response to this program. For example, a user would restrict his orders to local restaurants and order from far away restaurants only when he gets the message that he is among the lucky chosen customers. This is an example of a nudge provided by the platform to alter customer behavior. It is possible that such strategies would naturally come out of our mechanism design framework. Besides, our framework will help answer the question of exactly for what purpose are these nudges or incentive programs being used - How do they affect the welfare of customers? How do they affect the revenue of the platform?
When applied to real-world scenarios, it is very likely that the solution coming out from theory would require a complex signaling scheme between the system operator and the play- ers. Naturally, human players would not be able to maneuver if such complex signaling schemes are implemented in practice. Additionally, many times the system operator does not have access to all the necessary information related to resource availability and implemen- tation. We will now discuss ways in which these communication protocols and information collection activities can be implemented in practice. Developments in Artificial Intelligence (AI) would play a key role in these aspects.
Today, big data analytics is a hot topic that has found applications in several domains such as manufacturing, commerce, healthcare, financial services, safety and security. It is being used to:
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Predict equipment failure: Machine data such as its year of manufacturing, make, model, log entries, sensor data, error messages, engine temperature, and other factors can be used to deploy maintenance more efficiently and predict the remaining optimal life and state of systems and components.
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Assess resource availability: In situations where it is hard to get direct access to re- source data, information from other sources such as user feedback can be analyzed to access this information.
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Anticipate customer demand: Data from focus groups, social media, and customer feedback, which comes in varying formats, can be used for product development, re- source deployment, and operation fine-tuning to improve customer experience.
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Identify high-value customers: Insights from customer choices and spending patterns can be used to identify types of customers and use this information to target marketing strategies accordingly.
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Optimize merchandising: Analyzing data from mobile apps, in-store purchases, and geolocations will help improve inventory management and consequently encourage cus- tomers to complete purchases.
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Perform pricing analytics: Transaction data and information about supply and demand will help improve pricing strategies.
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Provide personalized recommendations: Data collected from repeated interactions with the customers can be used to provide offers that are fine-tuned to their requirements.
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Detect irregularity: Data from past behavior patterns can be used to detect fraud by identifying irregular transactions, to avoid accidents by detecting irregular driving patterns, or to caution customers against decisions they might consider irresponsible if they were in a different emotional state.
Notice how most of these tasks can be conveniently stated in our framework based on game theory, economics and behavioral psychology. Indeed, predicting equipment failure and assessing resource availability are related to the system operator gathering information about the environment such as capacity constraints in the model discussed in Chapter 2 or the allocation set and the mappings from allocation to outcomes in the mechanism design framework discussed in Chapter 6. Optimizing merchandising is a related task where the system operator actively influences the resource availability. Anticipating customer demand and identifying high-value customers relates to learning the type of players. Personalized recommendations and pricing are a part of the communication protocol between the players and the system operator. Detecting irregularity and fraud are a by-product of our behavioral approach that would help improve safety and security.
The learning tasks above such as gathering information about the environment or the players behavior and needs will involve taking advantage of the huge data collected through repeated interactions between the players and the systems, data coming from sensors and other unstructured sources like natural language or images. AI techniques such as Machine Learning (ML) and Reinforcement Learning (RL) algorithms aim to solve these problems. These algorithms require the designer to provide an objective function to maximize or a loss function to minimize. Also the communication framework is often assumed fixed exogenously either in an ad hoc fashion or relying on the designer’s experience. Our holistic approach will not only guide the design of these objective functions and communication protocols but it will also incorporate the network effects coming from strategic interaction between the players that are often missing from AI studies. Rarely is it true that the decisions and policies matter to a single individual without affecting other players in the system. Our framework will allow us to approach these problems in a principled manner and give rise to end-to-end solutions that are interpretable, efficient, and robust. Ideally, we want AI to be an accessory that would help implement the market-based strategy coming out of our framework in a practical manner by circumventing the complexity in the proposed solution. This will necessarily give rise to approximate solutions and methods from approximation theory, computation theory, and complexity theory that have gained prominence in computer science will be of special interest.