Human Buyers Research Articles

Speed traps: algorithmic trader performance under alternative market balances and structures

Using double auction market experiments with both human and agent traders, we demonstrate that agent traders prioritising low latency often generate, sometimes perversely so, diminished earnings in a variety of market structures and configurations. With respect to the benefit of low latency, we only find superior performance of fast-Zero Intelligence Plus (ZIP) buyers to human buyers in balanced markets with the same number of human and fast-ZIP buyers and sellers. However, in markets with a preponderance of agents on one side of the market and a noncompetitive market structure, such as monopolies and duopolies, fast-ZIP agents fall into a speed trap. In such speed traps, fast-ZIP agents capture minimal surplus and, in some cases, experience near first-degree price discrimination. In contrast, the trader performance of slow-ZIP agents is comparable to that of human counterparts, or even better in certain market conditions.

AI vs. Human Buyers: A Study of Alibaba’s Inventory Replenishment System

Inventory management is one of the most important components of Alibaba’s business. Traditionally, human buyers make replenishment decisions: although artificial intelligence (AI) algorithms make recommendations, human buyers can choose to ignore these recommendations and make their own decisions. The company has been exploring a new replenishment system in which algorithmic recommendations are final. The algorithms combine state-of-the-art deep reinforcement learning techniques with the framework of fictitious play. By learning the supplier’s behavior, we are able to address the important issues of lead time and fill rate on order quantity, which have been ignored in the extant literature of stochastic inventory control. We present evidence that our algorithms outperform human buyers in terms of reducing out-of-stock rates and inventory levels. More interestingly, we have seen additional benefits amid the pandemic. Over the last two years, cities in China partially and intermittently locked down to mitigate COVID-19 outbreaks. We have observed panic buying from human buyers during lockdowns, leading to the bullwhip effect. By contrast, panic buying and the bullwhip effect can be mitigated using our algorithms due to their ability to recognize changes in the supplier’s behavior during lockdowns. History: This paper has been accepted for the INFORMS Journal on Applied Analytics Special Issue—2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research.

Demand-Response Control in Smart Grids

In the smart grid, electricity price is a key element for all participants in the electric power industry. To meet the smart grid’s various goals, Demand-Response (DR) control aims to change the electricity consumption behavior of consumers based on dynamic pricing or financial benefits. DR methods are divided into centralized and distributed control based on the communication model. In centralized control, consumers communicate directly with the power company, without communicating among themselves. In distributed control, consumer interactions offer data to the power utility about overall consumption. Online auctions are distributed systems with several software agents working on behalf of human buyers and sellers. The coordination model chosen can have a substantial impact on the performance of these software agents. Based on the fair energy scheduling method, we examined Vickrey and Dutch auctions and coordination models in an electronic marketplace both analytically and empirically. The number of software agents and the number of messages exchanged between these agents were all essential indicators. For the simulation, GridSim was used, as it is an open-source software platform that includes capabilities for application composition, resource discovery information services, and interfaces for assigning applications to resources. We concluded that Dutch auctions are better than Vickrey auctions in a supply-driven world where there is an abundance of power. In terms of equity, Dutch auctions are more equitable than Vickrey auctions. This is because Dutch auctions allow all bidders to compete on an equal footing, with each bidder having the same opportunity to win the item at the lowest possible price. In contrast, Vickrey auctions can lead to outcomes that favor certain bidders over others, as bidders may submit bids that are higher than necessary to increase their chances of winning.

AI vs. Human Buyers: A Study of Alibaba's Inventory Replenishment System

AI vs. Human Buyers: A Study of Alibaba's Inventory Replenishment System

AI and Procurement

Problem definition: In this research, we study how buyers’ use of artificial intelligence (AI) affects suppliers’ price quoting strategies. Specifically, we study the impact of automation—that is, the buyer uses a chatbot to automatically inquire about prices instead of asking in person—and the impact of smartness—that is, the buyer signals the use of a smart AI algorithm in selecting the supplier. Academic/practical relevance: In a world advancing toward AI, we explore how AI creates and delivers value in procurement. AI has two unique abilities: automation and smartness, which are associated with physical machines or software that enable us to operate more efficiently and effectively. Methodology: We collaborate with a trading company to run a field experiment on an online platform in which we compare suppliers’ wholesale price quotes across female, male, and chatbot buyer types under AI and no recommendation conditions. Results: We find that, when not equipped with a smart control, there is price discrimination against chatbot buyers who receive a higher wholesale price quote than human buyers. In fact, without smartness, automation alone receives the highest quoted wholesale price. However, signaling the use of a smart recommendation system can effectively reduce suppliers’ price quote for chatbot buyers. We also show that AI delivers the most value when buyers adopt automation and smartness simultaneously in procurement. Managerial implications: Our results imply that automation is not very valuable when implemented without smartness, which in turn suggests that building smartness is necessary before considering high levels of autonomy. Our study unlocks the optimal steps that buyers could adopt to develop AI in procurement processes.

A Portfolio Strategy Design for Human-Computer Negotiations in e-Retail

ABSTRACT Human–computer negotiation has the potential to play an important role in today’s highly dynamic online environment, especially in business-to-consumer e-commerce transactions. However, the lack of research on effective automated negotiation algorithms to respond to human buyers’ strategic and/or tactic offers has limited the development of automated human–computer negotiation systems for real-world applications. Intelligent software agents that are capable of dynamically adjusting their negotiation strategy in response to human buyers’ offers can greatly improve the negotiation experience of human buyers. In this study, guided by design science principles, we design a portfolio strategy model, which implements four negotiation strategies (i.e., time-dependent, behavior-dependent, dynamic time-dependent, and impasse resolution) as the core of our software agent for negotiating with human buyers. To evaluate this novel model, we implement a prototype of the system and compare it with three benchmark single-strategy models (i.e., competitive, collaborative, and selection) in human–computer negotiation experiments. The results show that our model not only enables the software agent to outperform its human counterpart but also significantly increases the settlement ratio and the joint outcome of both parties.

AI and Procurement

In a world advancing toward Artificial Intelligence (AI), we explore how AI creates and delivers value in procurement. AI has two unique abilities: automation and smartness, which are associated with physical machines or software that enable us to operate more efficiently and effectively. In this research, we study how buyers' usage of AI affects suppliers' price quoting strategies. Specifically, we study the impact of automation — i.e., the buyer uses a chat-bot to automatically inquire prices instead of asking in person — and the impact of smartness — i.e., the buyer signals the usage of a smart AI algorithm in selecting the supplier. We collaborate with a trading company to run a field experiment on an online platform in which we compare suppliers' wholesale price quotes across female, male, and chat-bot buyer types under AI and no recommendation conditions. We find that, when not equipped with a smart control, there is price discrimination against chat-bot buyers who receive a higher wholesale price quote than human buyers. However, signaling the use of a smart recommendation system can effectively reduce suppliers' price quote for chat-bot buyers. We also show that AI delivers the most value when buyers adopt automation and smartness simultaneously in procurement.

Degrees of Rationality in Agent-Based Retail Markets

The imperfect decision-making of human buyers participating in retail markets varies from fundamental models that assume rational economic choices: even in markets with identical items human buyers are not rational, i.e., buyers do not always choose the cheapest option. Recent developments in artificial intelligence and e-commerce enable market participation by software agents that are (almost) perfectly rational due to their computational capacity. However, the increasing degree of buyers’ rationality might have unfavorable effects on retail markets with regards to the competition between sellers and the resulting prices. In this paper, we study the effects of varying degrees of buyers’ rationality on the competition and the prices buyers face in retail markets with identical items. We use the multinomial logit function to model different degrees of buyers’ rationality. We further model the competition between sellers using k-level reasoning: each seller computes the price to offer (best response strategy) with regards to its belief for the competition. First, we derive an analytical best response strategy (price) of a seller given the competing prices and the degree of buyers’ rationality, and show that there exists an optimal degree of buyers’ rationality that minimizes the price. Last, we use evolutionary game theory to show that perfect rationality leads to unstable competition dynamics increasing the overall cost for buyers. In contrast, bounded rationality leads to smoother dynamics and lower cost for buyers. Our insights raise the need to revisit design objectives for software agents in retail markets in light of their wider systematic impact.

Search deterrence in experimental consumer goods markets

In consumer goods markets, theory shows that it is generally profitable for sellers to use search-deterrence strategies to alter buyer search. These results rely on agents’ reacting solely to the economic content of these pressure tactics, ignoring any behaviorally based responses search deterrence may evoke. To test the validity of this assumption, this paper examines an experimental market where profit-maximizing strategy dictates that sellers should exercise one form of search deterrence, exploding offers. Sellers demonstrate a reluctance to use such offers against human buyers, but they are less reluctant to use them against computerized buyers. Human buyers are three times more likely to deviate from optimal strategy by rejecting rather than accepting these offers. Survey responses are consistent with other-regarding-preference-based reasons for sellers’ actions but not buyers’. Taken together, these results suggest the benefits of tactics that rely on pressuring decision-makers may be more nebulous than previously thought.

Supply Risk Mitigation Via Supplier Diversification and Improvement: An Experimental Evaluation

Due to the trend towards decentralization and greater complexity in supply chains, companies are increasingly exposed to supply risks. Various strategies to mitigate supply risks have been developed using modeling- based approaches, including risk diversification by dual sourcing and direct investment to improve supplier performance. Yet, despite the overwhelming evidence that managerial decisions are influenced by behavioral factors particularly under risk and uncertainty, such behavioral factors are typically not considered by previous theoretical studies. In this paper, we use controlled lab experiments to evaluate the performances of dual sourcing and single sourcing with supplier improvement strategies to mitigate risks of a buyer facing suppliers with different costs and risk profiles, and develop behavioral theories to elucidate the decision-making process under supply risks more effectively. With dual sourcing, human buyers do not diversify their orders effectively (relying on a more even allocation of orders between suppliers than theory suggests) and exhibit quantity hedging behavior. To explain this phenomenon, we propose and empirically validate a behavioral theory in which human buyers choose order quantities to minimize their disutility from order allocation errors between suppliers. Human buyers use the single sourcing with supplier improvement strategy relatively effectively, despite being subject to supplier selection errors due to bounded rationality.

Enhancing comparison shopping agents through ordering and gradual information disclosure

The plethora of comparison shopping agents (CSAs) in today's markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs' expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers' prices, potentially resulting in a more attractive "best price". In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer's tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.

Complexity as a Contract Design Factor: A Human‐to‐Human Experimental Study

Despite being theoretically suboptimal, simpler contracts (such as price‐only contracts and quantity discount contracts with limited number of price blocks) are commonly preferred in practice. Thus, exploring the tension between theory and practice regarding complexity and performance in contract design is especially relevant. Using human subject experiments, Kalkancı et al. (2011) showed that such simpler contracts perform effectively for a supplier interacting with a computerized buyer under asymmetric demand information. We use a similar set of experiments with the modification that a human supplier interacts with a human buyer. We show that human interactions strengthen the supplier's preference for simpler contracts. We find that suppliers have fairness concerns even when they interact with computerized buyers. These fairness concerns tend to be even stronger when suppliers interact with human buyers, particularly when the complexity of the contract is low. We also find that suppliers are more prone to random decision errors (i.e., bounded rationality) when interacting with human buyers. In the absence of social preferences, Kalkancı et al. identified reinforcement and bounded rationality as key biases that impact suppliers' decisions. In human‐to‐human experiments, we find evidence for social preference effects. However, these effects may be secondary to bounded rationality.

Taxation and Market Power

AbstractWe analyze the incidence and welfare effects of unit sales tax increases in experimental monopoly and Bertrand markets. We find, in line with economic theory, that firms with no market power are able to shift a high share of the tax burden to consumers, independent of whether buyers are automated or human players. In monopoly markets, a monopolist bears a large share of the burden of a tax increase. With human buyers, however, this share is smaller than with automated buyers, as the presence of human buyers constrains the pricing behaviour of a monopolist. Several control treatments corroborate this finding.

Search More, Disclose Less

The blooming of comparison shopping agents (CSAs) in recent years enables buyers in today's markets to query more than a single CSA while shopping, thus substantially expanding the list of sellers whose prices they obtain. From the individual CSA point of view, however, the multi-CSAs querying is definitely non-favorable as most of today's CSAs benefit depends on payments they receive from sellers upon transferring buyers to their websites (and making a purchase). The most straightforward way for the CSA to improve its competence is through spending more resources on getting more sellers' prices, potentially resulting in a more attractive ``best price''. In this paper we suggest a complementary approach that improves the attractiveness of the best price returned to the buyer without having to extend the CSAs' price database. This approach, which we term ``selective price disclosure'' relies on removing some of the prices known to the CSA from the list of results returned to the buyer. The advantage of this approach is in the ability to affect the buyer's beliefs regarding the probability of obtaining more attractive prices if querying additional CSAs. The paper presents two methods for choosing the subset of prices to be presented to a fully-rational buyer, attempting to overcome the computational complexity associated with evaluating all possible subsets. The effectiveness and efficiency of the methods are demonstrated using real data, collected from five CSAs for four products. Furthermore, since people are known to have an inherently bounded rationality, the two methods are also evaluated with human buyers, demonstrating that selective price-disclosing can be highly effective with people, however the subset of prices that needs to be used should be extracted in a different (and more simplistic) manner.

Taxation and Market Power

We analyze the incidence and welfare effects of unit sales taxes in experimental monopoly and Bertrand markets. We find, in line with economic theory, that firms with no market power are able to shift a high share of a tax burden on to consumers, independent of whether buyers are automated or human players. In monopoly markets, a monopolist bears a large share of the burden of a tax increase. With human buyers, however, this share is smaller than with automated buyers as the presence of human buyers constrains the pricing behavior of a monopolist.

Costly Buyer Search in a Differentiated Products Model: An Experimental Study

This study examines whether the content of buyer information and the timing of its dissemination affects seller market power. We construct laboratory markets with differentiated goods and costly buyer search in which sellers simultaneously post prices. The experiment varies the information on price or product characteristics that buyers learn under different timing assumptions (pre‐ and postsearch), generating four information treatments. Theory predicts that price information lowers the equilibrium price, but information about product characteristics increases the equilibrium price. That is, contrary to intuition, the presence of informed buyers may impart a negative externality on other uninformed buyers. Data support the model's negative externality result when sellers face a large number of robot buyers that are programmed to search optimally. Observed prices conform to the model's comparative statics and are broadly consistent with predicted levels. With human buyers, however, excessive search instigates increased price competition, and sellers post prices that are significantly lower than predicted.

Taxation and Market Power

We analyze the incidence and welfare effects of unit sales tax increases in experimental monopoly and Bertrand markets. We find, in line with economic theory, that firms with no market power are able to shift a high share of the tax burden to consumers, independent of whether buyers are automated or human players. In monopoly markets, a monopolist bears a large share of the burden of a tax increase. With human buyers, however, this share is smaller than with automated buyers, as the presence of human buyers constrains the pricing behaviour of a monopolist. Several control treatments corroborate this finding.

Strategic buyers, horizontal mergers and synergies: An experimental investigation

This paper reports an experiment designed to evaluate interrelationships between strategic buyers, market power and merger-induced synergies. The experiment consists of 40 posted-offer quadropolies. Treatments include the use of simulated or human buyers, seller consolidations and merger-induced fixed cost and unit cost synergies. In the simulated-buyer markets we observe behavior generally consistent with comparative static predictions: prices rise post-merger, and unit (but not fixed) cost synergies may exert some price-moderating effect. The addition of powerful buyers changes results markedly. Although prices are lower in the human buyer markets, outcomes are more variable and predicted comparative static effects are no longer observed.

Dissertation abstract: Essays on pricing in experimental duopoly markets

This dissertation comprises three independent essays that analyze pricing behavior in experimental duopoly markets. The first essay examines whether the content of buyer information and the timing of its dissemination affects seller market power. We construct laboratory markets with differentiated goods and costly buyer search in which sellers simultaneously post prices. The experiment varies the information on price or product characteristics that buyers learn under different timing assumptions (pre- and post-search), generating four information treatments. Theory predicts that price information lowers the equilibrium price, but information about product characteristics increases the equilibrium price. That is, contrary to simple intuition, presence of informed buyers may impart a negative externality on other uninformed buyers. The data support the model's negative externality result when sellers face a large number of robot buyers that are programmed to search optimally. Observed prices conform to the model's comparative statics and are broadly consistent with predicted levels. With human buyers, however, excessive search instigates increased price competition and sellers post prices that are significantly lower than predicted. The second essay uses experimental methods to demonstrate the anti-competitive potential of price-matching guarantees in both symmetric and asymmetric cost duopolies. When costs are symmetric, price-matching guarantees increase the posted prices to the collusive level. With asymmetric costs, guaranteed prices remain high relative to prices without the use of guarantees, but the overall ability of guarantees to act as a collusion facilitating device depends on the relative cost difference. Fewer guarantees, combined with lower average prices, suggest that cost asymmetries may discourage collusion. The third essay investigates the effect of firm size asymmetry on the emergence of price leadership in a homogeneous good duopoly. With discounting, the unique subgame-perfect equilibrium predicts that the large firm will emerge as the endogenous price leader. Independent of the level of size asymmetry, the laboratory data indicates that price leadership by the large firm is one of the most frequently observed timings of price announcement. In most cases, however, it comes second to simultaneous price-setting. This tendency to wait for the other firm to announce its price is especially strong when the level of size asymmetry between firms is low. We attribute the lower than expected frequency of price leadership to coordination failure, which is further compounded by elements of inequity aversion.

MARKOV DECISION APPROACH FOR TIME-CONSTRAINED TRADING IN ELECTRONIC MARKETPLACE

The recent advent in Internet technology has made electronic trading increasingly popular. Nevertheless, existing electronic trading systems still heavily rely on human decision making. In order to facilitate the new trading environment, one of the main trends is to employ autonomous agents as representatives of human buyers and sellers. In particular, given a user requirement and an imposed deadline, an autonomous agent has to search for possible deals from an electronic marketplace on behalf of its owner. One problem such an agent must face is: given a collection of offers and the remaining time, should I accept the current best offer, or continue to search for a better one, at a risk of losing the current offers? Similar time-constrained trading problems have been studied long time ago by the Markov decision process community. This paper alters the formulation by adopting assumptions suitable for electronic trading environments, and then derive the optimal trading strategy in terms of the agent's expected utility. Three optimization methods are proposed in order to reduce the infinite state space into the one with a manageable size. Experimental results verify the effectiveness of the methods for practical use.