Anirban Mukherjee

Marketing, Samuel Curtis Johnson Graduate School of Management, 

Cornell University, Ithaca, NY 14850

Email: am253@cornell.edu

Google Scholar: https://scholar.google.com/citations?user=V7wCZ5EAAAAJ&hl=en 

Homepage: https://www.anirbanmukherjee.com 

ORCID: https://orcid.org/0000-0001-6381-814X 

SSRN: https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=431500 

Select Publications

1. Chang, Hannah H., Anirban Mukherjee, and Amitava Chattopadhyay. More voices persuade: The attentional benefits of voice numerosity. Journal of Marketing Research 60, no. 4 (2023):  687-706. Direct link.

• The authors posit that in an initial exposure to a broadcast video, hearing different voices narrate (in succession) a persuasive message encourages consumers’ attention and processing of the message, thereby facilitating persuasion; this is referred to as the voice numerosity effect. Across four studies (plus validation and replication studies)—including two large-scale, real-world data sets (with more than 11,000 crowdfunding videos and over 3.6 million customer transactions, and more than 1,600 video ads) and two controlled experiments (with over 1,800 participants)—the results provide support for the hypothesized effect. The effect (1) has consequential, economic implications in a real-world marketplace, (2) is more pronounced when the message is easier to comprehend, (3) is more pronounced when consumers have the capacity to process the ad message, and (4) is mediated by the favorability of consumers’ cognitive responses. The authors demonstrate the use of machine learning, text mining, and natural language processing to process and analyze unstructured (multimedia) data. Theoretical and marketing implications are discussed.

2. Moon, Sungkyun, Kapil R. Tuli, and Anirban Mukherjee. Does disclosure of advertising spending help investors and analysts?  Journal of Marketing 87, no. 3 (2023): 359-382. Direct link.

• Publicly listed firms have discretion to disclose (or not) advertising spending in their annual (10-K) reports. The disclosure of advertising spending can provide valuable information because advertising is a leading indicator of future performance. However, estimates of advertising spending are available from data providers, arguably mitigating the need for its formal disclosure. This article argues that firms’ disclosure of advertising spending provides more complete and public information and therefore lowers investor uncertainty about future firm performance (idiosyncratic risk). Empirical analyses show that the effect is largely driven by the negative effect of disclosure of advertising spending on analyst uncertainty. Consistent with agency theory, the negative effect of the disclosure of advertising spending on analyst uncertainty is stronger for firms with more financial resources, firms with lower disclosure quality, and firms that are in more competitive industries. Additional analyses show that the disclosure of advertising spending has a significant positive effect on firm value in specific sectors. These results, therefore, identify an avenue for chief marketing officers to play a greater role in managing investor relations. In addition, they suggest strong merit for the Securities and Exchange Commission and the Financial Accounting Standards Board to reconsider current regulations governing advertising spending disclosure.

3. Gielens, Katrijn, Marnik G. Dekimpe, Anirban Mukherjee, and Kapil R. Tuli. The future of private-label markets: A global convergence approach. International Journal of Research in Marketing 40, no. 1 (2023): 248-267. Direct link.

• Private-label (PL) shares are characterized by considerable heterogeneity across both countries and categories, not only in their current level, but also in the rate at which they are growing. This creates ambiguity about their remaining growth potential. To offer insights into the likely long-run PL shares, we take a forward-looking perspective by means of a convergence model. We apply the model to two unique datasets that together span more than 50 countries, both emerging and developed, across more than 70 CPG categories. We find evidence of partial PL convergence: even though PL shares will become more similar, part of the currently observed heterogeneity will persist. The future evolution in two key marketing instruments, new-product introductions by both NB manufacturers and retailers and the NB-PL price gap, is found to play a substantial role in shaping the global PL landscape of the future. This impact is not uniform, however, but depends on the category, and varies with the retail, economic and cultural context. In addition, the long-run impact of both marketing drivers differs from what is currently observed, suggesting that managers should not adhere too strongly to earlier practices when planning for the future.

4. Mukherjee, Anirban, and Vrinda Kadiyali. The competitive dynamics of new DVD releases. Management Science 64, no. 8 (2018): 3536-3553. Direct link.

• We study the market for new (movie) DVDs in the United States. Our demand model captures seasonality, freshness (i.e., time between theatrical and DVD release), and state dependence. We also develop a structural model of dynamic competition in which studios balance waiting for high-demand weeks, against reduced freshness, and against competitive crowding. We find that studios emphasize DVD revenues from larger movies (by theatrical revenue) over DVD revenues from smaller movies. Studios also emphasize revenue from consumers who prefer larger and fresher movies. These behaviors are consistent with managerial conservatism: studio executives forgo DVD revenues from smaller movies to ensure the DVD success of larger movies.

5. Mukherjee, Anirban, Ping Xiao, Li Wang, and Noshir Contractor. Does the opinion of the crowd predict commercial success? Evidence from Threadless. In Academy of management proceedings, vol. 1, p. 12728. Academy of Management Briarcliff Manor, 2018. Direct link.

• Crowdsourcing new products involves an open call for creative ideas. To select among submissions, crowdsourcing portals ask the community (the “crowd”) to voice its opinion. Does the voice of the crowd predict the commercial success of a new product? This is an open question because over a half a century of research in consumer behavior is inconclusive on how peoples’ expressed attitudes predict their behavior. We study this question on a pioneering crowdsourcing portal, Threadless.com. We collect and examine a large-scale dataset tracking about 150,000 designs from 45,000 designers that received almost 150 million votes from 600,000 users between 2004 and 2010. We find that the counts of positive and neutral votes are consistent predictors of sales. However, the count of negative votes is an inconsistent predictor of sales – receiving more negative votes is associated with higher sales from the users who cast the votes, but lower sales from the users who did not cast the votes. These findings are consistent with users strategically voting down their best competitors to improve their odds of being selected.

6. Tuli, Kapil R., Anirban Mukherjee, and Marnik G. Dekimpe. On the value relevance of retailer advertising spending and same-store sales growth. Journal of Retailing 88, no. 4 (2012): 447-461. Direct link.

• In response to recent calls to study factors that determine a retailer's stock price, this study draws on signaling theory to examine the impact of two key marketing metrics that are widely disclosed by retailers to investors, advertising spending and growth in same-store sales (COMPS), and highlights the moderating role of various firm- and sector-specific factors. Using a stock-response model estimated on a sample of 1,646 observations for 257 retailers, the authors find that the value relevance of advertising spending and COMPS depends on the financial condition of, and the competitive pressures faced by, the retailer. In addition, the positive effect of COMPS on stock returns is found to be stronger in the presence of decreases in advertising spending.

7. Mukherjee, Anirban, and Vrinda Kadiyali. Modeling multichannel home video demand in the US motion picture industry. Journal of Marketing Research 48, no. 6 (2011): 985-995. Direct link.

• The U.S. motion picture industry has become increasingly reliant on posttheatrical channel profits. Two often-cited drivers of these profits are cross-channel substitution among posttheatrical channels and seasonality in consumer preferences for any movie. The authors use a differentiated products version of the multiplicative competitive interaction model to investigate these two phenomena. They estimate the model using data from 2000 and 2001 on two posttheatrical channels in the U.S. market: purchase and rental home viewing channels. Contrary to expectations based on business press commentary, after controlling for seasonality and movie attributes, the authors find low cross-channel price and availability elasticity for both channels. To measure the extent of cross-channel cannibalization, they simulate a 28-day window of sequential release with either purchase or rental channel going first. They find that windowing reduces the sum of revenues across both channels, because more consumers choose to not purchase or rent when faced with older movies in their favored channel rather than to switch to the alternative channel with newer movies.

Select Working Papers

1. Addressing Dynamic and Sparse Qualitative Data : A Hilbert Space Embedding of Categorical Variables (with Hannah H. Chang), https://arxiv.org/pdf/2308.11781.pdf 

• We propose a novel framework for incorporating qualitative data into quantitative models for causal estimation. Previous methods use categorical variables derived from qua- litative data to build quantitative models. However, this approach can lead to data-sparse categories and yield inconsistent (asymptotically biased) and imprecise (finite sample bia- sed) estimates if the qualitative information is dynamic and intricate. We use functional analysis to create a more nuanced and flexible framework. We embed the observed categories into a latent Baire space and introduce a continuous linear map—a Hilbert space embedding—from the Baire space of categories to a Reproducing Kernel Hilbert Space (RKHS) of representation functions. Through the Riesz representation theorem, we establish that the canonical treatment of categorical variables in causal models can be transformed into an identified structure in the RKHS. Transfer learning acts as a catalyst to streamline estimation—embeddings from traditional models are paired with the kernel trick to form the Hilbert space embedding. We validate our model through comprehensive simulation evidence and demonstrate its relevance in a real-world study that contrasts theoretical predictions from economics and psychology in an e-commerce marketplace. The results confirm the superior performance of our model, particularly in scenarios where qualitative information is nuanced and complex.

2. AI Knowledge and Reasoning: Emulating Expert Creativity in Scientific Research (with Hannah H. Chang),  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4738442, https://arxiv.org/pdf/2404.04436.pdf 

• We investigate whether modern AI can emulate expert creativity in complex scientific endeavors. We introduce novel methodology that utilizes original research articles published after the AI's training cutoff, ensuring no prior exposure, mitigating concerns of rote memorization and prior training. The AI are tasked with redacting findings, predicting outcomes from redacted research, and assessing prediction accuracy against reported results. Analysis on 589 published studies in four leading psychology journals over a 28-month period, showcase the AI's proficiency in understanding specialized research, deductive reasoning, and evaluating evidentiary alignment—cognitive hallmarks of human subject matter expertise and creativity. These findings suggest the potential of general-purpose AI to transform academia, with roles requiring knowledge-based creativity become increasingly susceptible to technological substitution.

3. Baire Space Embeddings: Handling Missing, Dynamic, and Sparse Data

4. Bridging the Gap: Using Interpretable AI to Incorporate Real-World Product Descriptions in Consumer Research Experiments (with Hannah H. Chang  and Sachin Gupta)

• This paper presents and demonstrates a novel AI-driven research design for consumer experiments. Conventional experiments often require the use of simplified, abbreviated, and stylized stimuli; constraints that may limit the realism, generalizability, and practical relevance of findings. In contrast, the proposed approach enables the use of myriad real-world, unstructured product descriptions as stimuli without simplification, abbreviation, or stylization. To process experimental data using the proposed method, the authors develop an innovative, interpretable AI model that they term labGPT. Comprising a partitioned deep learning neural network paired with a foundational large language model, labGPT generates low-dimensional, interpretable numerical representations of unstructured verbal descriptions. These representations are then employed in a statistical model of consumer responses. Theory testing is conducted by examining model estimates. To demonstrate the practical application and benefits of the proposed design, the authors study preference dynamics in a choice experiment with 1,000 consumers, who are shown about 50,000 wine descriptions randomly sampled from almost 120,000 wines in the market. Results suggest that the proposed design can improve the realism and external validity of consumer experiments by bridging the gap between the laboratory-based information environment and the real-world marketplace.

5. CAVIAR: Categorical Variable Embeddings for Accurate and Robust Inference (with Hannah H. Chang), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4787016 , https://arxiv.org/pdf/2404.04979.pdf 

• Social science research often hinges on the relationship between categorical variables and outcomes. We introduce CAVIAR, a novel method for embedding categorical variables that assume values in a high-dimensional ambient space but are sampled from an underlying manifold. Our theoretical and numerical analyses outline challenges posed by such categorical variables in causal inference. Specifically, dynamically varying and sparse levels can lead to violations of the Donsker conditions and a failure of estimation functionals to converge to a tight Gaussian process. Traditional approaches, including the exclusion of rare categorical levels and principled variable selection models like LASSO, fall short. CAVIAR embeds the data into a lower-dimensional global coordinate system. The mapping can be derived from both structured and unstructured data, and ensures stable and robust estimates through dimensionality reduction. In a dataset of direct-to-consumer apparel sales, we illustrate how high-dimensional categorical variables, such as zip codes, can be succinctly represented, facilitating inference and analysis.

6. Cognitive Boundaries of Narrating Voices in Persuasive Videos (with Hannah H. Chang)

7. Dalal Street Blues: The Socio-Economic Environment and the Demand for Bollywood Movies (with Ping Xiao), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4776156 

• How far are we a product of our times—does what we watch vary with the macro environment? In this study, we investigate the influence of the socio-economic environment on movie demand in India. Through a detailed analysis of data describing revenues by movie theater, movie, and week, for all multiplex (multi-screen) movie theaters and all movies in India, we establish the influence of escapism (i.e., selective exposure to media to escape from reality) and positional consumption (i.e., consumption to obtain status) as key determinants of demand. Incorporating a rich set of attitudinal and economic measures, and accounting for variation in movie quality, market demand, and seasonality, we find that hard economic times increase the demand for more escapist movies. Conversely, during such times, demand decreases in theatrical locations where attendance is scarcer and hence more positional. Generalizing the results, our data suggest that the election of Narendra Modi in 2014, which ushered in a wave of economic optimism, decreased the demand for more escapist movies.

8. Describing Rosé: An Embedding-Based Method for Measuring Preferences (with Hannah H. Chang), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3859740 

• Many products and services are best (and typically) described in prose. In extant preference-measurement methods, however, due to the challenge of numerically representing prose in econometric models, products can only be described to participants and portrayed in the utility model as a list of attributes. In this research, the authors develop an embedding-based utility model and preference method that addresses this limitation; in it, products are described to participants in (unstructured) prose. The proposed method provides three benefits: (1) in it, products can be described more completely, (2) it improves study realism, and (3) it enables a more detailed measurement of preferences. The authors employ the proposed method to measure consumer preferences in Australia, New Zealand, and the United States for wines made in 427 wine-growing regions in 44 wine-growing countries, from 708 wine-grape varietals. They find the proposed model has superior in-sample fit and generates better out-of-sample predictions than benchmark models. Importantly, the method is able to capture differences in consumers’ valuation for wines (products) that are observationally equivalent in categorical attributes, and therefore indistinguishable in classical categorical variable-based analysis. The use of the proposed model as a decision support system for marketing activities is demonstrated.

9. Does the Crowd Support Innovation? Innovation Claims and Success on Kickstarter (with Cathy Yang, Ping Xiao, and Amitava Chattopadhyay), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3003283 

• Online crowdfunding is a popular new tool for raising capital to commercialize product innovation. Product innovation must be both novel and useful (1-4). Therefore, we study the role of novelty and usefulness claims on Kickstarter. Startlingly, we find that a single claim of novelty increases project funding by about 200%, a single claim of usefulness increases project funding by about 1200%, and the co-occurrence of novelty and usefulness claims lowers funding by about 26%. Our findings are encouraging because they suggest the crowd strongly supports novelty and usefulness. However, our findings are disappointing because the premise of crowdfunding is to support projects that are innovative, i.e. that are both novel and useful, rather than projects that are only novel or only useful.

10. Fido's Ball: An Application of the Yule-Simon Process to Generating and Categorizing Qualitative Independent Variables

11. Forecasting in Rapidly Changing Environments: An Application to the U.S. Motion Picture Industry (with Vrinda Kadiyali), https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6010&context=lkcsb_research 

• Markets with rapidly changing environments provide forecasting challenges because of fewer similarities between past and future outcomes. In this paper, we provide a methodology that enables forecasting with relatively short histories. The application is to the U.S. motion picture industry where we forecast revenues in theatrical, sales (DVD and VHS), and rental channels. Using short market histories of similar products, we account for (1) observed and unobserved movie-specific characteristics, (2) seasonality of demand, (3) competition within and across multiple distribution channels (4) market expansion, substitution and complementarity between movies inside and across distribution channels. We extend the multiplicative competitive interaction model (Cooper and Nakanishi (1988)) to multiple distribution channels and build a novel two-step estimation method that allows for endogenous release schedules. We find our model outperforms existing models in most cases.

12. Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption (with Hannah H. Chang), https://arxiv.org/pdf/2403.09404.pdf, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4754533 

• Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguish between the ‘instrumental’ use of heuristics to match resources with objectives, and ‘mimetic absorption,’ whereby heuristics man- ifest randomly and universally. Through a series of innovative experiments, including variations of the classic Linda problem and a novel application of the Beauty Contest game, we uncover trade-offs between maximizing accuracy and reducing effort that shape the conditions under which AIs transition between exhaustive logical processing and the use of cognitive shortcuts (heuristics). We provide evidence that AIs manifest an adaptive balancing of precision and efficiency, consistent with principles of resource-rational human cognition as explicated in classical theories of bounded rationality and dual-process theory. Our findings reveal a nuanced picture of AI cognition, where trade-offs between resources and objectives lead to the emulation of biological systems, especially human cognition, despite AIs being designed without a sense of self and lacking introspective capabilities.

13. Intellectual Property Piracy and the Intersectionality of Artistic Merit, Gender, and Race (with Hannah H. Chang), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4750624 

• We pivot from traditional theories of intellectual property piracy that focus on financial drivers—such as pricing, accessibility, and affordability—to investigate the intersectionality of artistic merit, gender, and race. Utilizing an 18-year dataset, we examine the illicit release (leaking) of films at the Academy of Motion Picture Arts and Sciences, the organization responsible for the Oscars. Despite stringent safeguards, 54% of films, amounting to $41 billion in production expenditures and $66 billion in U.S. box-office revenues, were leaked between 2003 and 2020. Employed interlocked hypotheses and falsification tests, we show the leak of films aligned with increased access to high-quality content featuring historically marginalized gender groups. We do not observe similar findings for historically marginalized racial groups. Films recognized for artistic excellence and those featuring white female Oscar nominees were more likely to be leaked. The impact of white female nominees exceeded that of white male nominees; both groups exceeded that of non-white nominees. We contrast such findings with leaks in for-profit channels where patterns align with traditional financial factors but not the intersectionality of artistic merit, gender, and race. Our findings invite a reassessment of intellectual property management strategies, advocating for a more balanced approach incorporating factors such as fairness and cultural inclusivity.

14. Is Volunteering a Gateway to Increased Monetary Giving? Evidence from a Field Experiment (with Sachin Gupta and Sungjin Kim)

• This paper presents a field experiment conducted in the context of a nonprofit organization to investigate the causal link between volunteering and monetary giving. The study involved 149,480 individuals with no prior volunteering or giving history with the nonprofit. The treatment group received additional emails as part of a campaign designed to encourage participation as a volunteer in an upcoming citizen science event. Approximately seven weeks after the citizen science event, a fundraising campaign was launched, soliciting monetary donations. The study compares donation behaviors between the treatment and control groups. We find that the marketing intervention of additional emails increased volunteer participation by 10%. It also led to a 32% increase in donation participation rate but did not affect the average amount donated by each participant. These findings are consistent with volunteering affecting the extensive margin (the decision to donate) but not the intensive margin (the amount donated) of donation behavior. Our paper makes three key contributions. First, it provides novel evidence on the causal relationship between volunteering and monetary giving. Second, it utilizes core marketing activities, such as outreach emails, for causal identification of behavioral spillovers, thereby proposing a novel identification strategy. Third, it documents the asymmetric effects of volunteering on the extensive versus intensive margins of giving behavior. The results have important implications for the strategic integration of volunteer management and fundraising efforts within nonprofits and offer guidance for the optimal design of solicitation campaigns.

15. Plebeian Bias: Selecting Crowdsourced Creative Designs for Commercialization (with Ping Xiao, Hannah H. Chang, Li Wang, and Noshir Contractor), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3038775 

• We identify a new phenomenon – “Plebeian bias” – in the crowdsourcing of creative designs. Stardom, an emphasis on established individuals, has long been observed in many offline contexts. Does this phenomenon carry over to online communities? We investigate a large-scale dataset tracking all submissions, community votes on submissions, and revenues from commercialized submissions on a popular crowdsourcing portal, Threadless.com. In contrast to stardom, we find that the portal selects designs from “Plebeians” (i.e. users without an established fan base and track record) over “Stars” (i.e. users with an established fan base and track record). The tendency is revenue and profit sub-optimal. The evidence is consistent with incentives for the portal to demonstrate procedural fairness to the online community.

16. Psittacines of Innovation? Assessing the True Novelty of AI Creations,  https://arxiv.org/pdf/2404.00017.pdf, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4764101 

• We examine whether Artificial Intelligence (AI) systems generate truly novel ideas rather than merely regurgitating patterns learned during training. Utilizing a novel experimental design, we task an AI with generating project titles for hypothetical crowdfunding campaigns. We compare within AI-generated project titles, measuring repetition and complexity. We compare between the AI-generated titles and actual observed field data using an extension of maximum mean discrepancy—a metric derived from the application of kernel mean embeddings of statistical distributions to high-dimensional machine learning (large language) embedding vectors—yielding a structured analysis of AI output novelty. Results suggest that (1) the AI generates unique content even under increasing task complexity, and at the limits of its computational capabilities, (2) the generated content has face validity, being consistent with both inputs to other generative AI and in qualitative comparison to field data, and (3) exhibits divergence from field data, mitigating concerns relating to intellectual property rights. We discuss implications for copyright and trademark law.

17. Safeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformation, https://arxiv.org/pdf/2403.14706.pdf, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4739488 

• Generative AI has ushered in the ability to generate content that closely mimics human contributions, introducing an unprecedented threat: Deployed en masse, these models can be used to manipulate public opinion and distort perceptions, resulting in a decline in trust towards digital platforms. This study contributes to marketing literature and practice in three ways. First, it demonstrates the proficiency of AI in fabricating disinformative user-generated content (UGC) that mimics the form of authentic content. Second, it quantifies the disruptive impact of such UGC on marketing research, highlighting the susceptibility of analytics frameworks to even minimal levels of disinformation. Third, it proposes and evaluates advanced detection frameworks, revealing that standard techniques are insufficient for filtering out AI-generated disinformation. We advocate for a comprehensive approach to safeguarding marketing research that integrates advanced algorithmic solutions, enhanced human oversight, and a reevaluation of regulatory and ethical frameworks. Our study seeks to serve as a catalyst, providing a foundation for future research and policy-making aimed at navigating the intricate challenges at the nexus of technology, ethics, and marketing.

18. Scaling Marketing Research: Investigating Humor in Social Media and Video Advertising (with Hannah H. Chang) 

• Humor is a significant determinant of social media and video advertising effectiveness. However, its study in real-world marketplaces is limited by cost and effort considerations, whereby field data is coded using human assistants. In this research, we investigate the use of artificial intelligence (AI) to automatically code humor and other related variables in large-scale unstructured data. We showcase our developed measures, establishing the accuracy of state-of-the-art automated machine learning systems. We demonstrate that the direct inclusion of AI-coded variables in econometric models can lead to inconsistent and imprecise estimates, resulting in inaccurate inference. The source of these concerns is correlations between data features and variable coding errors that are likely, if not inevitable, as AI is underpinned by the internal numerical representations (i.e., embeddings) of data features. To address the issue, we develop novel estimators based on statistical properties measured in an auxiliary, smaller-scale sample, which permit both accurate causal inference and precise counterfactual predictions. Our methodology is designed to be both computationally stable and highly efficient, crucial attributes for inference and analysis in big data. We show that our approach yields far larger effect sizes for humor than biased estimators based on the direct inclusion of AI-coded variables, illustrating the importance of measuring and accounting for AI-coding error in the marketing research process.

19. Silico-centric Theory of Mind (with Hannah H. Chang),  https://arxiv.org/pdf/2403.09289.pdf, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4751507 

• Theory of Mind (ToM) refers to the ability to attribute mental states, such as beliefs, desires, intentions, and knowledge, to oneself and others, and to understand that these mental states can differ from one’s own and from reality. We investigate ToM in environments with multiple, distinct, independent AI agents, each possessing unique internal states, information, and objectives. Inspired by human false-belief experiments, we present an AI (‘focal AI’) with a scenario where its clone undergoes a human-centric ToM assessment. We prompt the focal AI to assess whether its clone would benefit from additional instructions. Concurrently, we give its clones the ToM assessment, both with and without the instructions, thereby engaging the focal AI in higher-order counterfactual reasoning akin to human mentalizing–with respect to humans in one test and to other AI in another. We uncover a discrepancy: Contemporary AI demonstrates near-perfect accuracy on human-centric ToM assessments. Since information embedded in one AI is identically embedded in its clone, additional instructions are redundant. Yet, we observe AI crafting elaborate instructions for their clones, erroneously anticipating a need for assistance. An independent referee AI agrees with these unsupported expectations. Neither the focal AI nor the referee demonstrates ToM in our ‘silico-centric’ test.

20. The Impact of Macro Socio-Economic Drivers and Fiscal Policy on Expenditure Allocation and Attribute Preferences (with Andre Bonfrer), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2189381 

• Our article investigates the effect of macro socio-economic drivers on Australian households’ allocation of expenditure in a category (household appliances) and conditional on the allocated category expenditure, preferences for products (clothes washers) within the category. At the category-level, we quantify the effect of changes in social mobility, disposable income, housing prices and the 2009 stimulus payments on purchase propensity and expenditure. At the product-level, we investigate how households trade off between price, energy efficiency and loading capacity conditional on allocated category expenditure, measuring nonhomotheticity in preferences. We use the model to study a number of hypothetical scenarios, where we simulate the effect of changes in macro socio-economic drivers and fiscal policies on market structure and revenue.