Initial evidence for biased decision-making despite human-centered AI explanations

In explainable artificial intelligence (XAI) research, explainability is widely regarded as crucial for user trust in artificial intelligence (AI). However, empirical investigations of this assumption are still lacking. There are several proposals as to how explainability might be achieved and it is an ongoing debate what ramifications explanations actually have on humans. In our work-in-progress we explored two posthoc explanation approaches presented in natural language as a means for explainable AI. We examined the effects of human-centered explanations on trust behavior in a financial decision-making experiment (N = 387), captured by weight of advice (WOA). Results showed that AI explanations lead to higher trust behavior if participants were advised to decrease an initial price estimate. However, explanations had no effect if the AI recommended to increase the initial price estimate. We argue that these differences in trust behavior may be caused by cognitive biases and heuristics that people retain in their decision-making processes involving AI. So far, XAI has primarily focused on biased data and prejudice due to incorrect assumptions in the machine learning process. The implications of potential biases and heuristics that humans exhibit when being presented an explanation by AI have received little attention in the current XAI debate. Both researchers and practitioners need to be aware of such human biases and heuristics in order to develop truly human-centered AI.

Sales Disclosure and Pricing Policies in the Presence of Social Learning

This study analyzes the role of sales disclosure and social learning (SL) in firms’ optimal responsive pricing policies and profits. If sales quantities are disclosed, potential customers will increase (decrease) their willingness to pay for the product based on the observation of relatively high (low) sales. In a monopoly market, a firm can control initial sales through the initial price, thus influencing consumers’ SL outcomes. We find that disclosing sales quantities and enhancing SL are always beneficial for a firm in a monopoly context. With an increase in the intensity of SL, a monopoly firm has a higher incentive to decrease the initial price of a product to attract early buyers, which is beneficial for consumers. However, consumer surplus may decrease if consumers’ purchase intentions are strongly driven by historical sales quantities. In a duopoly market, learning based on historical sales quantities can encourage potential customers but intensify competition between firms. Thus, in a competitive market, sales disclosure and SL are only beneficial to firms when consumers’ intrinsic valuation of a product is relatively low. Otherwise, SL harms firms.

Dynamic Demand and Pricing Inventory Model for Non-Instantaneous Deteriorating Items

In this model, an inventory model for deteriorating products with dynamic demand is developed under time-dependent selling price. The selling price is supposed to be a time-dependent function of initial price of the products and the permissible discount rate at the time of deterioration. The object is sold with the constant rate in the absence of deterioration and is the exponential function of discount rate at the time; deterioration takes place. Here, the demand not only dependent on the selling price but also on the cumulative demand that represents the saturation and diffusion effect. First, an inventory model is formulated to characterize the profit function. The Classical optimization algorithm is used to solve the optimization problem. The objective is to maximize the total profit of the retailers with respect to the initial selling price and cycle time. Concavity of the objective function is discussed through graphs. At last, a sensitivity analysis is performed by changing inventory parameters and their impact on the decision variables i.e. (initial price, cycle time) together with the profit function.

Surge Pricing and Two-Sided Temporal Responses in Ride Hailing

Problem definition: We investigate surge pricing in ride-hailing platforms from a temporal perspective, highlighting strategic behavior by riders and drivers and that drivers respond to surge pricing much more slowly than riders do. Academic/practical relevance: Surge pricing in ride-hailing platforms is a pivotal and controversial subject. Despite abundant anecdotal evidence, strategic behavior by riders and drivers has not been formally studied in the literature. Methodology: We adopt and analyze a classic two-period, game-theoretical model as in the strategic consumer literature. Results: We identify two types of equilibrium pricing strategies. The first consists of a short-lived, sharp price surge followed by a lower price, which we refer to as skimming surge pricing (SSP). The second consists of a low initial price followed by a higher price, which we refer to as penetration surge pricing (PSP). We find that PSP equilibria are generally superior to SSP equilibria when both exist but require platforms to share demand–supply information with drivers. Managerial implications: The SSP equilibrium rationalizes the controversial sharp surge-pricing practice: the short-lived sharp price surge causes many high-value riders to voluntarily wait out the initial surge period, which attracts additional drivers to the region to serve riders at a much lower price than the initial surge price. The theoretically superior PSP equilibrium suggests that a vastly different approach may improve surge pricing and highlights the potential value and importance for platforms to share demand–supply information with drivers.

Lessons from Sovaldi: Fueling Innovation While Ensuring Access

The case of Gilead Science’s hepatitis C therapy Sovaldi is a notable precursor to the larger debate over drug pricing, providing some important lessons, at a time when a new generation of potentially curative therapies are coming to market. In recent years, the pharmaceutical industry has pointed the finger at misaligned incentives, problems with payer benefit designs, a distorted rebate model, and hospital mark-ups as the cause of pricing and access issues. In order for progress to be made, the industry must acknowledge that it is the one entity across the supply chain that is responsible for establishing the initial price of a new medicine. Pointing to others without shouldering responsibility is misleading at best and intellectually dishonest at worst. Those that manufacture and commercialize medicines must play a leadership role in developing meaningful solutions to long-standing pricing issues. Pharmaceutical companies must demonstrate and articulate the value medicines deliver to all stakeholders. Taking a value-based approach, being cognizant of affordability issues, and delivering innovative medicines that address significant unmet medical needs is an important first step. Failing to do so, will leave the industry with a tarnished reputation and the likelihood of others implementing blunt instruments that will address these long-standing issues while threatening the life blood of the industry, innovation.

Learning from House Prices: Amplification and Business Fluctuations

We formalize the idea that house price changes may drive rational waves of optimism and pessimism in the economy. In our model, a house price increase caused by aggregate disturbances may be misinterpreted as a sign of higher local permanent income, leading households to demand more consumption and housing. Higher demand reinforces the initial price increase in an amplification loop that drives comovement in output, labor, residential investment, land prices, and house prices even in response to aggregate supply shocks. The qualitative implications of our otherwise frictionless model are consistent with observed business cycles and it can explain the economic impact of apparently autonomous changes in sentiment without resorting to non-fundamental shocks or nominal rigidity.

Back to the Origins of the Initial Public Offerings Price Range: Underwriter-Funds Network and Information Production Timeline

Although the initial price range in U.S. Initial Public Offerings (IPOs) is constrained by SEC regulations, a non-negligible percentage of IPO price ranges falls outside the ‘safe harbour’. We investigate how the price range - which sends the very first signals on the IPO quality to the market - is set in the due diligence phase, with special attention to unexplored networking patterns between underwriters and institutional investors. By making use of a Mixture Model applied to 1,246 US firms listed between 2004 and 2016, we show that underwriters that are centrally positioned in their network of regular investors are more likely to set a price range that is compliant with SEC guidelines. We argue that the flexibility resulting from being safe harbour-compliant allows underwriters to preserve their reputation for fair dealing with issuers by exploiting a dumping ground proviso or quid pro quo agreements with their network funds. Despite information produced by network funds in the due diligence step having no significant effect on the width of the price range, in our study, we provide evidence that the range does serve as a proxy of the uncertainty of the listing firms.

An Extended Speculation Game for the Recovery of Hurst Exponent of Financial Time Series

The speculation game is an agent-based toy model to investigate the dynamics of the financial market. Our model has achieved the reproduction of 10 of the well-known stylized facts for financial time series. However, there is also a divergence from the behavior of real market. The market price of the model tends to be anti-persistent to the initial price, resulting in the quite small value of Hurst exponent of price change. To overcome this problem, we extend the speculation game by introducing a perturbative part to the price change with the consideration of other effects besides pure speculative behaviors.

Demonstration of Maize-Nougcake Concentrate Based Sheep Fattening in Horro District, Oromia Regional State, Ethiopia

This activity was conducted in Horro district of western Oromia, Ethiopia with the objective of popularizing proven sheep fattening technology. The study sites were Leku and Gitilo kebeles where community based sheep breeding project is underway. The rams to be fattened were weighed; body condition was recorded, de-wormed and sprayed against internal and external parasites. Initial price of the rams was estimated by a panel of three price informed estimators. The rams were supplemented with 400 g/h/day concentrate composed of 49.5% Noug cake, 49.5% ground maize, 1% salt for 90 days fattening period. The mean initial weight, final weight, initial price and selling prices of the rams were 21.05 kg, 28.51 kg, 517 birr and 1577.5 birr, respectively. Total body weight gain (TWG) of the rams on average was 7.5 kg over the fattening period with a range of 4.5-13.5 kg. The average daily gain (ADG) for the fattening period was 83.3 g/h/ day. The difference between initial and final body weight (7.5 kg) and initial and final body condition (1.7 kg) were statistically significant (p=0.00) and (p=.04), respectively. On average, a net return of birr 456.7 was accrued to the farmers from sale of the fattened rams. An increase in input price by 10%, keeping the price of fattened rams constant would result in marginal return of 1.6 and a net return of 396 birr. The technology is still profitable on the face of the expected escalation in input prices. The escalation is counteracted by the current attractive sale price of fattened rams. It is thus important to go for further scaling up in areas where there is access to these inputs.

New B2B product pricing

Purpose The purpose of this paper is to investigate the characteristics that lead to the adoption of the three new business-to-business (B2B) product pricing strategies, namely, skimming pricing (i.e. a high initial price), penetration pricing (i.e. a low initial price) and pricing similar to competitive prices. Design/methodology/approach To achieve the study’s research objectives, data were collected through a mail survey from 116 B2B firms, operating in four different sectors. Findings The adoption of skimming pricing and penetration pricing is triggered by company-related factors that are associated with the company’s corporate and marketing strategy and the product characteristics, while the adoption of pricing similar to competitive prices is influenced by market-related factors that are associated with customers’ and competitors’ characteristics. Practical implications The above findings indicate that the managers responsible for setting prices for new B2B products should follow a “situation-specific approach” and be guided by the unique characteristics of their internal and external environment. Originality/value Its contribution lies on the fact that, building upon quests within the existing literature, it constitutes one of the first attempts to examine empirically the aforementioned issue.