Introduction to the second edition of 3f.

We propose a Neuro-Structured Learning framework (NSL), which incorporates the management of cognitive resources in finance education and professional training programs. The framework rests on: 1) a measurement foundation to track real‑time cognitive resource levels and depletion/recovery rates; 2) resource usage optimization, which reduces the cognitive costs of complex learnings tasks by “banking” elemental learning assets until they become low‑cost routines; and 3) resource generation and recovery, which prescribes “effective active recovery” (EAR) tasks and practices to rebuild capacity and optimize performance. The NSL provides a scalable architecture for courses, programs, and corporate training that accelerates the transition from novice to competent practitioner while reducing error rates, training waste, and burnout.

This presentation is not about a single paper but an overview of how FinTechs can benefit from collaborating with academics (drawing from two examples of my own research)

We study conditions under which tokenization creates value for indivisible realworld assets (RWAs). We show that tokenization does not create value merely by making an asset transferable, fractional, or digitally scarce. Value arises only when digital ownership records change the economics of ownership by leveraging on asset characteristics and past ownership records on the blockchain. This may generate retained value for past owners, create provenance value for later buyers, separate usage and financial rights, support membership benefits through fractional ownership, or strengthen post-sale incentives through royalties. These forces determine whether tokenized markets are inactive, thin but informative, lemons-like, or liquid but uninformative. We discuss implications for art and luxury tokens, private-equity and venture-capital tokens, real-estate tokens, tokenized claims on a social enterprise, and sustainable-firm tokens.

Consumer fintech has solved access. Anyone can open an account, connect their data, and see their balance. Understanding is where products still fall short.

Most users hesitate after onboarding. They see the dashboard, then drop off. Up to 60% abandon onboarding entirely, and Day-30 retention often sits in the single digits. The infrastructure works. The interpretation doesn't.

This session explores why activation and trust remain among the most underestimated problems in fintech, why generic AI recommendations fall short in financial environments, and how behavioral intelligence is emerging as the interpretation layer between raw transaction data and the products users actually rely on.

Drawing on the development of Neumetria, the talk covers how context-aware systems read cadence, drift, pay cycles, and behavioral posture in real time, and what that means for activation, retention, and the products being built across spending, investment, and crypto.

This article develops a taxonomy of stablecoin activity on the Ethereum blockchain. Using all ERC-20 transfers for USDT, USDC, and DAI for the period 2021–2025, we filter noise, Maximal Extractable Value (MEV) signatures and wash-like behaviours, then map the filtered transactions into directed networks and Louvain communities. We show that stablecoin usage cannot be reduced to manipulations and confirm that stablecoins are mainly settlement layers for crypto markets rather than transactional instruments. We also document institutional centralization for exchanges and competitive concentration for DeFi and fintech, highlighting that structural decentralization and economic decentralization are distinct dimensions of stablecoin network dynamics.

I construct a new measure of financial constraints by applying a large language model to narrative disclosures in firms' Management’s Discussion and Analysis from Form 10-K filings. The model evaluates each filing as a finance expert and classifies the firm's external financing difficulty on an ordered scale, producing the GenAI FC Index. The index captures contextual signals - such as nuanced liquidity discussions - that traditional accounting-based and prior text-based proxies often miss. It behaves sensibly in both the time series and cross-section and shows only moderate correlations with existing measures, indicating that it contains distinct information. Behavioral tests reveal that firms classified as constrained recycle far less equity and are substantially more likely to omit dividends, and less likely to initiate or increase them. Across these settings, the GenAI FC Index yields stronger and more consistent behavioral separation than benchmark text-based measures. The results demonstrate that generative AI can extract economically meaningful information about firms' financing frictions at scale.

We show that retail participation is increasingly rule-based rather than discretionary, leaving sharp and recurring intraday footprints in trading activity. Using transaction-level data from SPX zero-days-to-expiration (0DTE) options, we document pronounced volume spikes exactly at the hour and half-hour marks that emerge almost instantaneously and dissipate within seconds. These spikes intensify over time, are concentrated in complex multi-leg trades, and are largely absent in longer-dated contracts. Spike-time trading is dominated by small, standardized, short-premium strategies consistent with template-driven execution and mechanical risk budgeting, and we do not find evidence that these trades are systematically wealth-depleting. Around these deterministic windows, quoted spreads tighten while effective spreads widen, indicating intensified liquidity provision alongside higher execution costs. Overall, fintech-enabled retail automation generates synchronized order flow that reshapes intraday liquidity and market quality.

This paper employs a dataset containing a comprehensive international coverage of corporate
default events. The dataset is primarily constructed using large language models on
newspaper articles based on a dictionary of keywords that reflect corporate debt distress.
Based on this dataset, I show strong evidence of a common global latent risk (frailty) factor
that impacts corporate debt distress risk worldwide. The global latent risk factor identifies
substantial common variation among separately estimated dynamic latent risk (frailty) factors
of firms at the country level. Estimations of country frailty factors control for observable
firm fundamentals capturing systemic risk and omitted macroeconomic factors. Commonalities
among country frailty factors highlight global systemic risk. Observable global factors
and financing variables can only explain up to 25% of global frailty, indicating the vulnerability
of global corporate credit markets to common latent systemic risk. The findings also
detect cross-country corporate default risk spillovers, underscoring the international interconnectedness of corporate distress risk.

Cross-border payment networks are best understood not as static pipelines, but as dynamic, non-stationary graphs in which each routing decision reshapes the system itself - shifting liquidity, altering token prices, and changing the future feasibility and cost of available paths. Traditional routing and max-flow algorithms fail in this setting because they assume fixed capacities and independent edges, while real-world markets exhibit flow-dependent costs, endogenous feedback loops, and rapidly evolving state.

This work reframes cross-border FX and stablecoin-based settlement as a real-time control problem under uncertainty, where optimal decisions must account for both current conditions and the impact of execution on future network states. The approach integrates short-horizon forecasting with stochastic optimization to enable adaptive routing across chains, tokens, venues, and time in a continuously changing environment.

We study pricing efficiency in decentralized prediction markets by comparing market-implied probabilities from Polymarket with benchmarks derived from option-implied risk-neutral distributions extracted from the derivatives market. We study Bitcoin prediction bets and find that, although Polymarket prices broadly track option-implied benchmarks, they show systematic mispricing driven by complexity, behavioral factors, and market frictions. Mispricing is most pronounced in tail events, during periods of high volatility, major macroeconomic shocks, and reflects the influence of sentiment, attention, and blockchain-specific risks. These results reveal both efficiency and behavioral distortions in prediction markets.

Yes, by decreasing firm risk, not by increasing profitability, and with investors taking years to recognize the value created. We start, using novel AI patent data, by documenting significant corporate production of AI innovation as early as 1990. Then, we show that a signification motivation for a firm's AI production is the mutually reinforcing effects of the firm's innovation capacity (exogenous R&D stock) and its labor inputs' AI exposure (both the firm's own and its customers'). We use the interaction of these two effects to instrument for AI production. We find that producing AI creates firm value through a large, permanent decrease in risk (cash flow and stock return, systematic and idiosyncratic). Further evidence suggests that AI lowers physical capital intensity and increases bargaining power for producing firms. The initial market reaction to AI patent announcements is economically small, but abnormal stock returns thereafter are significantly positive (about 5% per year) for (only) roughly three years, suggesting initial undervaluation followed by gradual correction. We find no evidence of investor learning, except during the past five years. We empirically distinguish producing AI innovation versus AI adoption, automation, general technology, and other potential confounds.

We study how social media messaging affects asset markets using experimental methods. Participants trade in markets with asymmetric information, some markets with and some without the ability to send anonymous public messages. Rather than improving market efficiency through information sharing, we find that messaging facilitates profitable pump-and-dump strategies. Informed traders systematically post misleading messages to manipulate prices. These manipulation schemes are frequently successful, with price manipulators earning substantially more than other informed traders. We also observe deceptive strategies by uninformed traders, though these were generally unprofitable. Both successful and unsuccessful manipulation schemes reduced market efficiency, highlighting an important consequence of investors using social media for financial communication.

Financial time series forecasting is central to trading, portfolio optimization, and risk management, yet it remains challenging due to noisy, non-stationary, and heterogeneous data. Recent advances in time series foundation models (TSFMs), inspired by large language models, offer a new paradigm for learning generalizable temporal representations from large and diverse datasets. This paper presents the first comprehensive empirical study of TSFMs in global financial markets. Using a large-scale dataset of daily excess returns across diverse markets, we evaluate zero-shot inference, fine-tuning, and pre-training from scratch against strong benchmark models. We find that off-the-shelf pre-trained TSFMs perform poorly in zero-shot and fine-tuning settings, whereas models pre-trained from scratch on financial data achieve substantial forecasting and economic improvements, underscoring the value of domain-specific adaptation. Increasing the dataset size, incorporating synthetic data augmentation, and applying hyperparameter tuning further enhance performance.

The world is moving toward tokenized payments, with growing institutional adoption driven by increasing legal clarity. While blockchain-based payments offer speed and efficiency, they also raise concerns around financial stability, including risks of capital flight and reduced oversight in peer-to-peer models. This session explores how these challenges can be addressed. It presents how Centiglobe enables direct cross-border payments between regulated banks and payment institutions through a collaborative payment network powered by network-based tokenized deposits. This approach allows institutions to capture the benefits of tokenization while operating within existing regulatory frameworks, supporting G20 objectives on speed, cost, and transparency.

This paper develops a tractable model for the cryptocurrency prices based on the classical framework for rational bubbles. In the baseline equilibrium, investors hold cryptocurrency to sell them to future users. In a bubble equilibrium, investors hold cryptocurrency because they expect its price to appreciate due to future investment inflows. We establish the mathematical relationship between net investment flows and the cryptocurrency's rate of appreciation. The net investment flows required to sustain a bubble equilibrium increase in new coin issuance, the required return and the level of transactional demand, and temporarily decrease when transactional demand expands. The net investment inflows required to sustain a bubble equilibrium are, everything else equal, smaller for cryptocurrencies with a proof-of-stake than for cryptocurrencies with a proof-of-work protocol.

How does cross-country divergence in banking regulation shape domestic banking systems? We study whether an increase in host-country capital requirements significantly rebalances the competitive landscape toward global banks that are not subject to host regulation. Using novel Peruvian data on global bank lending that relies on representative offices, we uncover that this locally unregulated organizational form accounts for nearly 25% of corporate dollar credit. We find that higher host-country capital requirements significantly reshape credit allocation: banks outside host regulation expand by 7-10pp relative to locally regulated lenders, even for the same borrower.

Despite growing recognition that narratives shape economic outcomes, we lack a formal, empirically tractable definition of what constitutes a “story” or a “narrative.” This project develops a conceptual and computational framework to address that gap. I define a story as a temporally ordered sequence of economically meaningful events and a narrative as a set of stories sharing the same meaning. An axiomatic framework formalizes story similarity and disciplines narrative clustering. Empirically, I implement a three-stage pipeline: large language models extract structured event sequences from unstructured text; stories are compared using a multi-stage similarity measure that combines semantic and structural features; and network-based clustering aggregates similar stories into endogenously emerging narratives. I construct narrative-prominence indices which display systematic co-movement with financial indicators of interest (e.g., prices, returns, volatility, etc.), consistent with markets dynamically reweighting competing narratives over time. Interpreted through a revealed-preference lens, these patterns suggest markets act “as if” particular narratives are believed or attended to at different points in time. Ongoing work develops models of dynamic narrative credibility and evaluates whether narrative-based measures provide incremental forecasting power relative to existing text-based approaches. By providing both a formal definition and an empirically implementable identification strategy, the framework offers a new approach to studying belief formation and aggregate, market-level belief elicitation in financial markets.

We replicate Chinco, Clark-Joseph, and Ye (2019) and investigate how sensitive return predictability results are with respect to how returns are constructed. We find that 1-minute returns are predictable only if returns are constructed using the last quote recorded in each one minute interval, irrespective of which exchange posted the quote, i.e., when returns capture both time-series and cross-sectional variation across exchanges. Predictability is largely due to spikes in prices, leading to strong negative autocorrelations. Return predictability is significantly lower if we remove spikes or when using other methodologies to construct returns based on prices without spikes.

Cross-border remittances are often discussed as a private transfer problem, but for many communities they are a critical piece of local economic infrastructure. In Latin America, especially in rural and underserved regions, the real opportunity is not simply to move money from one country to another, but to ensure it arrives in a form that strengthens local participation, resilience, and access to formal financial services.

This talk will share practical lessons from Interledger Foundation partnerships in Mexico and the broader region, where interoperable payment infrastructure is helping connect remittance flows to local financial ecosystems. We will look at how open protocols can reduce friction for providers, enable local institutions to participate more effectively, and support financial inclusion beyond basic access. We will also explore why policy goals around inclusion, competition, and digital economy growth depend on infrastructure that can actually perform at scale and across networks.

The session will connect the technical design of interoperable payments with the lived realities of remittance recipients, rural financial institutions, and payment providers. It will show how open connectivity can help turn cross-border transfers into domestic economic capacity, and why trust, reliability, and interoperability are now central to the future of finance.

Although large-scale quantum computers are not yet available, their future development poses a potential threat to the cryptographic foundations of cryptocurrencies. As an upper bound, we estimate that by the end of 2024, approximately US$ 586 billion in the Bitcoin network alone could be potentially exposed. We then examine whether investors are aware of this risk by analyzing their response to advances in quantum computing. Conventional cryptocurrencies exhibit negative returns and higher trading volume following such news, whereas a quantum-robust cryptocurrency exhibits positive returns. Our findings suggest that some investors are aware of quantum computing risks and respond by shifting toward quantum-resistant cryptocurrencies.

How does online bank expansion (digital-only depository institutions that originate loans without human intermediation) affect consumer credit market structure? Using loan-level data from Germany, we show that online banks cherry-pick low-risk borrowers, generating adverse selection for traditional banks. We develop and test a model in which online banks have lower costs but weaker screening because they rely solely on hard information. Online banks offer substantially lower rates to low-risk borrowers, but this advantage declines with credit risk, creating a crossover point beyond which traditional banks become more competitive. Using historical branch density as an instrument, we show that supply-driven screening differences contribute to this pattern. Extending the framework to fintech lenders reveals market segmentation: online banks serve the lowest-risk borrowers, traditional banks the medium-risk segment, and fintechs the highest-risk segment. Over time, the traditional online rate gap widens, consistent with deteriorating borrower pools at traditional banks. A shift-share design---combining pre-determined district banking composition with national branch consolidation rates---provides causal support. Our findings highlight that technological development in credit markets can generate important distributional consequences.

We demonstrate how distortion of content arises endogenously when a content-curating or content-creating algorithm is rewarded for maximizing exposure to particular content regardless of user preferences. We show that when insights from the static model extend to the dynamic setting, characterizing trends in distortion due to fundamentally productive and counter-productive effects. Applying the framework to financial advice by Large Language Models (LLMs), we microfound user utility through portfolio choice. When users suffer from confirmation bias, optimal customization balances productivity enhancements with confirming preferences. Even when they do not, better information quality can paradoxically hurt the precision of users by relaxing their participation constraint, enabling more distortion that offsets the direct gains.

Theories of limited attention predict that investors rely on typical patterns to navigate high-dimensional firm information, making atypical firms hard to process. To quantify this difficulty, we propose a data-driven measure of how atypical a firm's combination of characteristics is using an autoencoder (ATYP). The model learns the typical patterns that describe most firms, and ATYP aggregates the deviations those patterns cannot explain. Unlike measures of disclosure or organizational complexity, ATYP captures the processing difficulty of the underlying information. Empirically, we document that ATYP strongly predicts future returns. A decile portfolio that sells high-ATYP firms and buys low-ATYP firms earns 1.47% per month (equal-weighted) and 0.82% (value-weighted). The effect strengthens precisely where investor attention is low and arbitrage is limited, suggesting mispricing as the explanation.

Current post-trade clearing systems rely almost exclusively on cash or cash-like collateral, leaving vast reserves of short-term liquidity embedded in trade credit outside formal settlement infrastructures. This paper introduces a clearing framework that integrates accounts receivable and payable (AR/AP) into secondary market settlement via the Cycles Protocol—a distributed, multilateral mechanism based on double-entry accounting and atomic cycle execution.

The Cycles Protocol acts as a clearing and settlement layer that reduces liquidity needs by using multilateral set-off and cycle removal to maximize balance-sheet compression. It operates without novation and complements, rather than replaces, CCPs’ margining, loss mutualization, and default management.

Compared with Liquidity-Saving Mechanisms (LSMs) in Real-Time Gross Settlement (RTGS) systems, this approach extends liquidity optimization beyond interbank payments to real-economy financing networks, reducing systemic reliance on scarce collateral and central intermediaries.

Flash loans are a DeFi primitive that allow users to borrow large amounts of capital without collateral, provided the loan is borrowed and repaid within a single atomic transaction. We ask how this form of on-demand, uncollateralized funding affects market liquidity and execution quality in automated market makers. Exploiting the token-level rollout of flash-loan eligibility on Aave as a natural experiment, we show that flash-loan introduction leads to a pronounced and persistent increase in Uniswap V1 liquidity: executable depth, total value locked, and trading activity rise relative to matched control pools, while slippage and volatility do not deteriorate in a sustained way. Using transaction-level classifications and high-frequency panel regressions, we further show that flash-loan intensity is positively associated with subsequent depth and TVL, with effects concentrated in large debt-restructuring and arbitrage loans. Overall, our evidence suggests that flash loans operate primarily as a liquidity-enhancing funding technology that deepens markets without worsening short-run execution quality, while reallocating risk toward passive liquidity providers.

We develop a model of banking competition where there is a partition between passive depositors
who always deposit funds at the same bank and active depositors who can observe and act on all
deposit rates in the market. This partition leads to two opposite business models where banks
choose either to be (i) a monopolistic bank and serve only passive depositors or (ii) a competitive
bank and also serve active depositors. Prudential regulation needs to account for its impact on
the relative attractiveness of these two business models. We show that this additional effect, of
banks switching between business models, can offset the traditional impact of capital requirements,
in that an increase in capital requirements can trigger an intensification of competition, which
in turn can increase overall risk-taking. Similarly, the imposition of a deposit-rate ceiling may
render the competitive business model comparatively more appealing. We also show that in this
situation the introduction of shadow banks has the potential to reduce rather than increase overall
risk-taking.

We study how automated investment rules affect saving behavior and investment outcomes using detailed data from a FinTech app designed to help retail investors access mutual funds. Users choose how to design these rules, which vary along dimensions such as frequency, amount, and triggering conditions. Using a randomized encouragement design, we show that automated rules causally increase average savings without crowding out manual contributions. We also show that automated rules reduce trend-chasing behavior: while manual deposits respond strongly to recent returns, automated ones do not, narrowing the gap between fund returns and realized investor returns. However, rule timing remains performance-sensitive—users tend to activate rules after periods of strong returns and suspend them during downturns, especially for equity funds. A survey deployed on the app user population reveals that adopters of automated investment rules are primarily motivated by a desire to avoid procrastination, reduce cognitive load, and simplify decision-making, while non-adopters cite preferences for flexibility and concerns about income volatility. Our findings highlight both the promises and the limitations of automation in improving individual financial outcomes.

The capital asset pricing model (CAPM) performs poorly empirically, as market risk (beta) is weakly related to average excess returns. In low news periods, iden tified using idiosyncratic news from the Dow Jones Newswire, market betas have a strong and positive relation with average returns. Higher beta firms earn lower returns to idiosyncratic news, and individual firm betas are consistently lower on days with idiosyncratic news. Consistent with an attention-based mechanism, the beta-return relation is positive when attention to market-wide idiosyncratic news is low relative to macroeconomic attention, and reverses when idiosyncratic attention is high. Hybrid “betting-against-beta” trading strategies exploiting these periods earn high returns. I conclude that waves of high aggregate idiosyncratic news obscure the performance of the CAPM at the firm level and significantly influence asset pricing.

Financial contracts are promises to pay, specifying who pays whom, how much, and when. These cashflows are the backbone of transaction processing, risk management, accounting, and other systems. The contractual cashflows are determined by a limited set of mathematical algorithms that contain inherent "certainty”. Nevertheless, the legal, academic, and financial communities have failed to recognize this “certainty”, leading to unstandardized representations of the same financial contracts across different systems and uses. This creates inefficiencies, vulnerabilities, and reconciliation burdens. We discuss how operationalizing "certainty" with standardized algorithms is essential for modernizing TradFi and providing the necessary foundation to scale DeFi ecosystems and integrate them with mainstream finance.

Flash loans provide uncollateralized, atomic leverage that eliminates traditional capital constraints on arbitrage. We document that flash-loan arbitrage exhibits substantially higher concentration than capital-funded arbitrage, with the top ten participants capturing approximately 50\% of volume compared to 20\% for traditional arbitrageurs. This pattern arises because atomic execution creates option-like payoffs with limited liability but reveals complete strategy information when broadcast publicly. Intense competition induces selection into costly private routing channels requiring non-transferable technological infrastructure. Our findings show that eliminating capital constraints shifts the binding constraint to technological capability, generating concentration through infrastructure requirements rather than financial capacity.

Governments deploy substantial resources to support innovative startups. Support effectiveness depends critically on which firms receive it, yet little is known about the allocation process. This paper introduces leader bias: public resources disproportionately flow to startups backed by leading investors for reasons unrelated to startup quality. I study leader bias in the context of government loan guarantees, an increasingly important tool for startup finance. My stylized model shows that leader bias arises under application frictions and imperfect observability of startup quality, diverting public resources away from constrained-yet-promising firms. Using confidential credit-registry data matched to venture capital (VC) records, I find that guarantees disproportionately go to startups backed by top-tier VCs. These recipients already enjoy easier credit access without guarantees, do not exhibit different credit risk or innovation output, and are weaker firms within top-tier VC portfolios. To probe mechanisms, I exploit sudden expansions of guarantee programs and find that startups backed by top-tier VCs benefit more from relationship lending and program know-how. A diffusion model provides plausibly causal evidence that program knowledge spreads through VC syndication networks and drives guarantee adoption. These findings have direct implications for program design. Overall, intermediation and informational frictions systematically shape the allocation of public resources.

This paper documents a novel cognitive source of disagreement: heterogeneous recall of past experiences due to context-dependent memory contributes to divergence in household beliefs. The intuition is illustrated through a stylized model à la Wachter and Kahana (2024). Empirically, using local weather as contextual cues, I construct a measure of memory-based disagreement in U.S. households’ inflation expectations and show that it explains survey-based disagreement across forecast horizons, with stronger effects when disagreement is driven by unusual contexts. The findings align with established memory regularities, generalize to households in other countries, and remain robust across alternative specifications. I further show that heightened disagreement arising from heterogeneous memory weakens monetary policy transmission. These results highlight a previously overlooked cognitive friction shaping disagreement in households’ expectations.

This paper derives ex-ante confidence intervals of stock risk premium forecasts that are based on a wide range of linear and Machine Learning models. Exploiting the cross-sectional variation in the precision of risk premium forecasts, I provide improved investment strategies. The confident-high-low strategies that take long-short positions exclusively on stocks with precise risk premium forecasts outperform traditional high-low strategies in delivering superior out-of-sample returns and Sharpe ratios across all models. The outperformance increases (decreases) with the model complexity (bias). The confident-high-low strategies are economically interpretable as trading strategies of ambiguity-averse investors who account for confidence intervals around risk premium forecasts.

The core argument: fintech growth doesn't primarily fail due to lack of capital or product-market fit. It fails because structural complexity accumulates quietly, and by the time it's visible, it's already a hard ceiling. Neither practitioners nor researchers have fully mapped this dynamic.

I'd draw on concrete experience: the bank subsidiary tension at Überseehub, where product ambition and institutional structure created a specific kind of friction that no playbook had prepared me for. And on patterns I now see across the fintechs I work with through Aegirion Ventures, where the same ceiling appears in different shapes, at different stages, in different markets.

The unrelenting growth in blockchain functionality drives greater adoption and, therefore, ensures the security of transaction settlement. We show that the wider adoption carries its own risk for the blockchain. Specifically, when a large fraction of blockchain transactions involves non-native tokens, the cost of price discovery in the native cryptocurrency increases. This, in turn, leads to lower trade informedness and price efficiency. In a difference-in-differences setup, we find that a shock to non-native transactions typically leads to 1.25 bp of unrealized informed price movement and a 0.60% decrease in price efficiency in the underlying cryptocurrency. Our findings lend support to the theoretical literature arguing that multiple attack vectors on blockchain arise from native cryptocurrency mispricing.

The current implementation of the Digital Euro, as proposed by the European Central Bank, raises serious concerns regarding public spending, competition, privacy, and monetary stability.

First, the scale of the proposed budgets is unjustified.

For example, the EUR 56 million allocated to the Alias Lookup

service (PRO-009485) is far beyond what comparable systems typically

require. This is particularly troubling given that standardized and

publicly available solutions already exist. At a minimum, this

raises questions about procurement discipline and value for money.

Second, the tender requirements are structurally

exclusionary. By designing the system to function only on the two

dominant proprietary mobile platforms, the ECB reinforces an

existing duopoly and marginalizes European and Free/Libre

open-source alternatives. This approach contradicts stated EU goals

of competition, digital sovereignty, and technological independence.

Third, the online Digital Euro does not meet citizens'

stated demand for payment privacy. Survey data commissioned by the

ECB itself shows that Europeans want digital cash. Instead, the

proposal delivers a capped, account-like instrument with unclear

liability structures. The lack of confidence in its usefulness is

reflected in the proposal to mandate merchant acceptance by law.

Finally, the offline Digital Euro requirements pose a

fundamental and non-negotiable problem. The combination of

full anonymity, offline operation, transferability, and zero risk to

the recipient and the central bank is not merely an open

engineering challenge --- it is mathematically incompatible.

This is a formally established result in cryptography: without

online reconciliation or a trusted authority, digital assets can

always be copied. No consumer hardware can change this fact;

hardware can only raise the cost of attack temporarily.

As a result, the ECB’s current requirements will result in a

dilemma: either (1) spend hundreds of millions of euros

pursuing an impossible goal, or (2) give up on privacy and

discharge unavoidable double-spending risks to citizens and

law-enforcement, or (3) deploy a vulnerable system with the ECB

remaining fully liable, risking monetary stability from

large-scale fraud, potentially by sophisticated or state-level

actors.

In the current geopolitical and cyber-security environment, this

represents a systemic risk to the Euro, not a technical

detail.

Taken together, these issues risk undermining trust in the Euro and in

the institutions responsible for safeguarding it.

We therefore urge the ECB to pause the current implementation,

reassess its procurement and design assumptions, and switch over to

known alternative solutions with a publicly vetted and

technically proven design, which do not have the same drawbacks as

outlined above. Europe deserves and needs a solution that is competitive,

privacy-preserving, technologically sound, and aligned with

democratic accountability.

Organizations increasingly want AI agents that retain context over time. However, building "agentic memory" requires moving beyond simple language models to complex systems that must autonomously extract, synthesize, and retrieve information. Because these systems are fundamentally non-deterministic, they introduce unique operational hurdles.

This talk explores the current landscape of agentic memory, covering its theoretical promise, common implementation approaches, and practical challenges—such as compounding errors, multi-user conflicts, and the loss of data provenance. Drawing on a decade of data engineering experience, the session offers a pragmatic guide on navigating these systems, highlighting when to invest in emerging frameworks and when to rely on simpler, observable alternatives.

We revisit the disposition effect and argue that it is best understood not as a primitive behavioral bias, but as a reduced-form outcome of stable investment styles. Using a unique inter-linked dataset that combines a large-scale experiment with real-world mutual fund transactions, we document strong within-investor persistence in disposition behavior across time and contexts. This persistence is largely driven by a fixed investment style: contrarian investors exhibit a substantially stronger disposition effect, while it is minimal for momentum investors. Investment style explains far more variation in the disposition effect than standard demographic and socioeconomic characteristics. By contrast, realization preference is generally shared. We provide some of the first field evidence that it accounts for roughly 10% of the bias via a sharp jump at the zero-return threshold. Overall, our findings suggest that the disposition effect often emerges as a structural outcome of price-based trading rules, rather than a generic behavioral bias.

This paper situates Germany’s current pension reforms within the broader context of financial wellbeing and ecosystem-building in the fintech sector. Against the backdrop of mounting demographic pressure and increasing individual responsibility for retirement provision, Germany’s occupational pension landscape is undergoing significant transformation. The second Betriebsrentenstärkungsgesetz (BRSG II, BGBl. 2026 I Nr. 14), effective since 22 January 2026, introduces opt-out occupational pension enrolment (bAV) via company agreements outside tariff-bound sectors from 1 July 2026 onward, including a mandatory 20% employer contribution. At the same time, the newly established Alterssicherungskommission (ASK) has been tasked with developing comprehensive recommendations for all three pillars of retirement provision by the end of Q2 2026.

Drawing on a quota-weighted survey of 5,020 employed adults across 13 industries in Germany (YouGov Panel, Q4 2025), fielded immediately before the reforms came into effect, the paper provides timely baseline evidence on occupational pensions, financial stress, and financial wellbeing. Five key findings emerge: (1) occupational pensions are the most widely offered employee benefit in Germany (40.5%), yet only 45.7% of employees actively contribute; (2) despite access, one in four eligible employees does not participate, while 69% report insufficient information and guidance; (3) participation rates differ substantially by company size, reaching only 38.8% in SMEs compared to 53.6% in large organisations; (4) participation in occupational pensions is associated with significantly higher CFPB Financial Wellbeing Scores (+5.5 points) and lower reported financial stress, although no causal inference is claimed; and (5) 38% of employees report experiencing financial stress — rising to 43% in the smallest firms — with uncertainty and loss of financial control identified as the dominant stressor (37%).

The paper argues that occupational pensions should not be viewed solely as a retirement instrument, but increasingly as a strategic component of employee financial wellbeing, talent retention, and economic resilience. It further discusses how ecosystem actors such as House of Finance and Tech Berlin contribute to bridging gaps between policymakers, financial institutions, fintechs, employers, and academia in shaping the future of retirement provision and financial wellbeing innovation in Germany and Europe.

Pledges to buy back tokens issued by platforms in decentralized finance are becoming increasingly common. We develop a tractable model for the exchange rates of platform tokens that incorporates user demand, investment demand, and buyback pledges. We derive closed-form solutions for the valuation of tokens and the time required to fulfill the pledge. Buyback pledges can increase the value of the tokens, but manipulation of the token supply by market participants may make the pledge more expensive than intended. Sufficiently aggressive buyback pledges induce purely financially motivated investment in platform tokens, which could trigger regulatory classification as securities.

The design of Central Bank Digital Currencies (CBDCs) plays a critical role in determining their adoption and economic impact. This paper presents evidence from a nationally representative survey of 10,000 UK residents assessing consumer willingness to adopt the proposed Digital Pound. We first document that, while overall adoption intent is modestly positive, it varies substantially by demographic group and transaction type: adoption peaks among individuals in their thirties, men, and those with higher income, education, or trust in institutions, and is strongest for low-stakes transactions such as online purchases and small everyday expenses. We then leverage a randomized information-treatment experiment to isolate the causal impact of key design features—such as interest payments, privacy protections, integration with banking apps, and government incentives— on demand. Interest-bearing functionality and financial i ncentives s ignificantly in crease adoption, particularly among consumers initially skeptical of CBDCs, followed by privacy considerations. By contrast, technology-enabled benefits a nd i ntegration w ith t he b anking s ector h ave l imited effects. These findings u nderscore t he c ritical r ole o f e ffective de sign an d ta rgeted po licies in expanding adoption and realizing the potential of CBDCs.

Most traders lose to themselves, not their edge: breaking their own rules, revenge trades, FOMO, fatigue. TradeZen builds your psychological profile and surfaces patterns in your own data (state, energy, recent trades and trading behaviour), flagging risk in real time where supported, or post-trade if you prefer. No correlation claims, no blocking.

On this panel we're pitching an early idea and asking for the sharpest possible feedback: where the thesis breaks, whether the signal even holds, who would actually pay, and whether this survives as trading goes programmatic.

Credit spreads are leading indicators of real activity but only capture pricing information. I show that quantity-based indicators, specifically net flow into open-end funds primarily investing in leveraged loans, provide additional predictive content. Using monthly indicators and conditioning on a broad set of controls, credit spreads reduce out-of-sample forecast errors in the next two years by up to 9%, while loan fund flow further reduces these errors by up to 7%. A dynamic corporate investment model with a slow-moving financing capacity state, which lags behind fast-moving fund flows, explains this price-quantity timing asymmetry in the forecasting power.

Bart Vanhaeren — CEO & Co-founder, YES (Young Early Starters)

Bart has spent 20 years building the infrastructure that lets people invest. At KBC Group he re-founded Bolero, growing Belgium's largest online broker past €10B in assets, and launched an award-winning crowdfunding platform. He then co-founded InvestSuite, the wealth-tech firm now powering front-to-middleware for banks and brokers across Europe.

With YES, Bart is taking on the problem none of those platforms could solve: the fact that by the time most people open their first brokerage account, they've already lost two decades of compounding and the financial habits that come with it. YES is the first investment platform built for children aged 8–18, where every trade is real, every trade is parent-approved, and every lesson is grounded in behavioural science.

Bart is at 3f to share what YES is learning at the frontier of children's investing and to find the academic collaborators who can help turn an unprecedented dataset into rigorous insight on how early practice shapes lifelong financial behaviour.

I analyze the joint dynamics of Bitcoin trading on exchanges and on the blockchain
(“settlement”) from 2009 to 2023. Aggregate trading and settlement exhibit a strong
shared trend both on a daily and intra-day basis. Monthly correlations average 40%,
though they vary significantly over time and across exchanges. In aggregate and for
regulated exchanges, but not for several unregulated exchanges, increased trading ac-
tivity leads to higher settlement both contemporaneously and in subsequent periods.
Ranking exchanges by how trading influences settlement provides a useful quality met-
ric: increased trading driving higher settlement indicates that exchanges or their clients
settle trades frequently, making the exchange less vulnerable to bank-runs and default.
On the contrary, I identify BTCC, LocalBitcoins, and Yobit as exchanges with elevated
default risk.

I examine how contactless tap-to-pay (TTP) adoption affects consumer spending using item-level transaction data from U.S. convenience stores. Exploiting staggered store rollouts within a triple-difference framework, I show that TTP raises monthly spending primarily by increasing transaction frequency; a phenomenon of consumption fragmentation. Crucially, while monthly totals rise, individual TTP transactions are smaller and contain fewer items. These effects are concentrated in impulse-sensitive categories like beverages, tobacco, and food service products. My findings suggest that reducing interface-level micro-frictions lowers payment salience and weakens self-control, demonstrating that even incremental fintech innovations fundamentally reshape the timing and composition of consumption.

This session offers an applied look at how CB Insights builds fintech and digital asset intelligence, tracking emerging categories including agentic payments, stablecoins, and neobanks using structured and unstructured datasets and composite predictive signals — spanning funding, firmographics, partnerships, valuations, patents, and headcount — across a universe of 12 million companies.

This paper studies whether asset management companies use customer data to attract capital. Exploiting information from their websites' codes, I track when fund managers begin collecting and analyzing data on their potential customers using tools like Google Analytics or A/B testing.

I show that funds adopting such technologies attract 1.5% higher annual flows and charge higher fees, despite no improvement in performance. These results are concentrated on retail share classes and decline with competition as more rival funds adopt similat tools. At the fund-family level, adopters expand their product offerings, and new fund focus more on retail-oriented themes. Within existing funds, I find evidence of changes in prospectus content and greater sales efforts rather than product differentiation.

Overall, data technologies allow managers to raise more capital and charge higher fees. These findings show that technological innovation in asset management extends beyonf portfolio allocation decisions, and it affects how funds attract and retain capital.

In a world where large language models like ChatGPT make information more accessible than ever, a critical question remains: how do we move from generated answers to reliable, structured intelligence—especially in high-stakes domains like finance?

This talk explores the gap between LLM-based exploration and data-driven decision-making, and introduces an alternative approach based on structured, real-time news data.

We will show how NewsAPI.ai, built on top of the Event Registry platform, processes over 150,000 global sources to transform unstructured articles into enriched, machine-readable signals—including entities, sentiment, categories, and event detection.

Key topics include:

The limitations of LLM-only approaches in financial and academic research How structured news data enables trend detection, risk monitoring, and signal extraction Event-based clustering and metadata enrichment as a foundation for analysis A brief comparison with GDELT, highlighting differences in data structure, usability, and analytical depth The role of MCP (Model Context Protocol) in connecting LLMs with structured news systems, enabling workflows such as scan → triage → retrieve

The session will demonstrate how combining LLMs with structured news infrastructure creates a more reliable and scalable approach to financial intelligence, research, and innovation.

Providing liquidity on decentralized exchanges earns fees but exposes liquidity providers (LPs) to impermanent loss from price movements. With concentrated liquidity, LPs control this risk by choosing how narrowly to deploy capital around the price. Using option prices, we quantify the cost of liquidity provision by developing measures of implied and realized impermanent loss for concentrated liquidity and define the associated impermanent loss risk premium. Empirically, higher expected impermanent loss widens liquidity ranges, while higher risk premia re-center liquidity around the spot price, highlighting opposing effects of risk and compensation.

We show that short-term fluctuations in firms’ ability to convert trade credit receivables into liquidity through factoring have large and persistent real effects, with limited substitution from other financing sources or adjustments in trade credit terms. In Brazil, specialized non-bank intermediaries (FIDCs) securitize receivables and are key providers of working capital financing. Using novel transaction-level data linking factoring, invoices, payments, credit operations, and employment records, we exploit investor inflows to FIDCs in a shift-share design to identify exogenous variation in factoring supply. A one-percentage-point decline in factoring rates increases factoring volumes by 16%, revenues by 6%, and intermediate input expenditure by 4%, with effects persisting for several months. Firms expand permanent employment and demand less temporary labor. A model of corporate liquidity management rationalizes these findings: factoring endogenously transforms production into collateral, tying firms’ real and financial decisions. Model-implied macro-elasticities indicate that lowering economy-wide factoring spreads by 1 percentage point would raise aggregate output and wages by 0.3 to 0.5 percentage points.

This study explores the elimination of information asymmetry in the European SME credit market through structural fintech innovation. By leveraging the mandated open-data architecture of PSD2 and PSD3, the research demonstrates how automated risk-assessment engines bypass legacy banking infrastructure using unified data-aggregation pipelines. The paper analyzes the integration of automated underwriting into digital marketplaces to achieve instant, data-driven credit configuration. Ultimately, this framework provides a scalable, asset-light methodology for executing efficient cross-border lending operations.

This paper shows that the equity market prices the novel social risks associated with generative AI. We exploit the release of ChatGPT as an information shock that updated investor beliefs about AI-related tail risks. Using pre-event ESG scores as proxies for firms' social risk management, we show that, controlling for AI productivity exposure, low-ESG firms underperform high-ESG firms by 4 percentage points over the two weeks following the release. The effect is concentrated in the Social pillar, particularly data privacy and security. Increases in option-implied downside risk indicate that changes in discount rates are the channel. A low-minus-high ESG portfolio of AI-exposed firms earns significant alphas during 2023–2024, suggesting that investors demand compensation for bearing AI-related downside risks. Our findings are consistent with a tail-risk model in which social risks of AI are priced through discount rates.

Safe transactions: AI-augmented payment security and monitoring network

This paper introduces Safe Transactions, an AI-augmented customer profiling and payment monitoring platform designed to address the vulnerabilities in banking ecosystems from the customers’ perspective. Traditional fraud detection is often limited to after the payment has been sent to the bank for remittance while customer profiling is legally limited to each bank’s unilateral relationships with its customers.

The network’s primary innovation lies in its multi-stream data architecture. First, it aggregates behavioral and historic patterns to establish a holistic identity verification model. Second, it incorporates actual data generated from pre-testing payments—analyzing transactions before they are formally submitted to payment institutions for remittance. By applying advanced machine learning algorithms to this combined dataset and agglomerated mapping against profiles the system identifies subtle anomalies and high-risk signatures that traditional, single-point monitoring fails to capture. This proactive framework significantly enhances the accuracy of fraud detection, identifies payment screening system alerts, both true and false positives, and ensures a higher integrity of the global remittance pipeline through collaborative, AI-driven intelligence.

The growing importance of social media in financial markets has amplified the role of financial influencers (“finfluencers”) in shaping investor attention and market dynamics. Yet little is known about how finfluencers gain and sustain influence in financial social networks. We conduct this study in the context of meme coin markets, a highly social and sentiment-driven segment of cryptocurrency markets. We examine influence formation in Reddit-based meme coin networks during a full boom–bust cycle (September 2024 to March 2025), using data from 103,882 posts, 609,904 comments, and 128,697 users across ten subreddits.

We model users, posts, comments, subreddits, and hourly market conditions as a heterogeneous graph and apply a Heterogeneous Graph Neural Network (HGNN) to predict user influence rankings. The HGNN framework allows us to jointly incorporate topological structure, content features, user attributes, and market states without manually specifying interaction patterns.

Our results show that influence mechanisms are strongly market-state dependent. During high-volatility periods, network position and engagement timing dominate, while content features lose predictive power. In contrast, content quality becomes important in stable markets. We further identify a highly skewed hierarchy of influence and distinguish sustained finfluencers from occasional ones, with persistence driven by consistent activity and central network positioning.

Present frameworks for assessing the performance of international financial centers primarily focus on enabling conditions: regulatory quality, infrastructure, and human capital. They are measured mainly through perception surveys. They capture what makes a center attractive but say little about what a center actually does. This paper, the second in a series on measuring international finance, proposes a functional approach. It defines four core dimensions of financial center performance: domestic impact, international contribution, international connectivity, and attractiveness. These span two analytical axes, scope of contribution (national versus international) and directionality of flows (inward-facing versus outward-facing), and are anchored in a foundation of enabling conditions. Three complementary factors, innovation capacity, reputation, and trust, as well as resilience, act as amplifiers and safeguards of functional performance. The paper develops detailed measurement frameworks for each complementary factor: innovation capacity is decomposed into knowledge generation, entrepreneurial ecosystem, regulatory innovation, and adoption and diffusion; reputation and trust into institutional trustworthiness, international standards compliance, market confidence signals, and perception and narrative; and resilience into diversification, agility, and institutional depth. Two practical instruments are derived from the framework. A diagnostic dashboard provides individual financial centers with a structured self-assessment throughout all dimensions, designed to reveal performance discrepancies and identify development opportunities. A four-stage typology from National Service Centers through Regional Gateways and International Specialists to Fully Integrated Global Hubs classifies financial centers by functional profile and maps characteristic strategic pathways, to be validated empirically through cluster analysis. The framework is explicitly designed not as a ranking but as a diagnostic and strategic tool for financial center stakeholders.

There is increasing awareness of how local media affects financial markets, but also of the endogeneity of media coverage. We separate the causal impact of local media on financial markets from selection effects using a new, hand-collected database of newspaper closures. We find that at least 29% of local newspaper closures are driven by distress, and thus, likely endogenous to local economic conditions. Return volatility and idiosyncratic risk decrease significantly after non-distress-driven newspaper closures, but increase after distress-driven closures, suggesting the presence of substantial selection effects. We find similar patterns for liquidity and trading. Once we account for selection, the estimated impact of local newspapers on volatility increases by over 40%. The reduction in volatility after non-distress-driven newspaper closures is larger for stocks subject to greater information frictions, lower national media coverage, firms located in remote areas, firms with a more concentrated geographic presence, and during recessions. All these tests suggest that investor information processing is the main channel that drives our results. Our findings highlight that the effect of media on financial markets may be larger than previously documented.

While crowdfunding and online investment platforms have expanded internationally, the ecosystem remains highly fragmented across jurisdictions, regulations, and technology platforms. Reliable market data is difficult to collect, platform information is often inconsistent, and comparing markets across countries remains challenging for both practitioners and researchers.

In this session, Konstantin Boyko, founder of LenderKit and CrowdSpace, will share practical observations from building software for investment platforms and working with crowdfunding ecosystem data across multiple markets. The presentation will focus on the real-world difficulties of data collection, platform classification, transparency, and cross-border discovery in alternative finance.

The talk will also highlight areas where closer collaboration between researchers and industry practitioners could improve understanding of the crowdfunding market and support more transparent and accessible financial ecosystems.

We introduce a novel, high-frequency measure of belief disagreement derived from blockchain-based prediction markets (Polymarket) to investigate the determinants of the U.S. Treasury convenience yield. Unlike traditional volatility indices (VIX) or news-based proxies (EPU), our measure captures ``skin-in-the-game'' divergence in trader beliefs regarding specific political outcomes. We document a striking asymmetry in how political disagreement affects the demand for safe assets across the maturity spectrum. Shocks to political disagreement significantly compress the convenience yield on short-term Treasury bills, consistent with operational risk and liquidity deterioration, while simultaneously expanding the premium on long-term bonds, reflecting a ``flight-to-safety'' motive. Our findings demonstrate that political discord generates a ``twist'' in the safe asset curve—acting as a liquidity shock in the near term but a safety trigger in the long run—and highlight the utility of prediction markets in isolating \textit{ex-ante} uncertainty from media attention.

We develop a transactions-based classification model to identify retail investors’ transactions by using a definitive identification of investor types as issued by the South Korean exchange. Logistic regressions and Random Forest models are trained with known investor types and classify daily retail trades on anonymized out-of-sample transactions. Both models deliver about twice higher classification accuracy than existing methods, particularly for small-cap stocks. Classification accuracy declines systematically with firm size. Because retail investors prefer small stocks, and illiquid stocks in all size quintiles, trade-based identification of retail trading activity is most reliable in small-cap and illiquid stocks.

We develop firm-level measures of input and output price changes using textual analysis of
earnings calls. We establish four facts: (1) Input prices increase (decrease) at the median
firm once every 7 (30) months. (2) Input price changes contain an equal blend of aggregate
and firm-specific components. (3) A firm’s stock price experiences a -1.15% return when our
input price change measure is in the top tercile of price increases. Using a structural model,
we relate firm markup to this stock price reaction. (4) Firms pass through 70% of their input
price changes to output prices in the same quarter.

The rapid evolution of financial technology is reshaping the global financial ecosystem faster than traditional education systems can adapt. While financial institutions, fintech companies, and regulators are accelerating innovation in AI, embedded finance, digital assets, RegTech, and open banking, a significant talent and knowledge gap continues to emerge between industry needs and workforce readiness.

This presentation introduces FNextAcademy, a practitioner-led educational initiative designed to bridge the gap between academic theory and real-world financial innovation. The academy focuses on equipping professionals, entrepreneurs, and future leaders with practical, hands-on knowledge across fintech, digital finance, regulation, artificial intelligence in finance, and emerging financial ecosystems.

The session will present a new framework for fintech education based on three core pillars: industry relevance, interdisciplinary learning, and future-oriented skill development. Unlike traditional finance education models, FNextAcademy integrates direct industry expertise, real use cases, strategic thinking, and practical implementation methodologies tailored to the rapidly changing financial sector.

The presentation will also discuss the growing global demand for continuous financial upskilling, the challenges faced by financial institutions in talent development, and the role of specialized fintech education platforms in enabling financial inclusion, innovation adoption, and responsible digital transformation.

The contribution combines practitioner experience, ecosystem observations, and educational innovation to propose a scalable model for preparing the future workforce of finance.

Shortly before expiration, Robinhood submits trades to close out the options positions of its customers who do not have the cash or shares to exercise their options or accept assignment. These liquidations result in bursts of customer trades at known times, and allow us to identify the underlying symbols and options positions popular with Robinhood customers. The liquidating trades face adverse execution, as options prices move in unfavorable directions. Underlying equity and ETF prices move in directions consistent with price pressure in the equity and ETF markets as options market makers execute delta hedge trades as they absorb the Robinhood order flow. Our results reveal how brokerage frictions in retail options trading impact options and underlying prices.

We investigate the transaction patterns of Bitcoin users by their geographical origin and design Random Forest architectures to obtain indices for the share of transactions per region. We show that North American and European users transaction flows have a stronger market relationship than global flows. Using the indices, trained on a unique dataset spanning 5 years of blockchain transactions and their associated geographical origin, we construct region-specific blockchain metrics which we show improve market modelling and prediction as compared to the global metrics. We further show with the indices regional differences in users willingness to pay transaction fees.

In this paper we model conditional distributions of intraday maximum and minimum REIT prices with extreme value theory (`EVT') techniques. We condition the parameters of these distributions on continuously evolving risk decomposition values derived from the CMBX market. These risk decompositions are interpreted as dynamic state variables, and serve as signals for changing likelihoods of daily REIT extrema. By assessing the model at fifteen-second intervals, intraday, our model generates high-confidence signals of single optimal stopping times for long/short trades for REITs in our study and extraordinary profits with positive and significant alphas in some 90% of our tests.

Programmability is at the heart of ongoing work on the future of money and payments by central banks around the world. Despite its potential, there is growing concern that programmability conflicts with the provision of “good” money. This paper overviews key principles of “good” money and argues that the discourse on programmability inadequately differentiates between programmable money, which is generally negatively viewed, and programmable payments, which is generally accepted as part of the future. We provide a framework for programmable monetary systems that sharply distinguishes between programmable money and programmable payments. We show that our framework nests a broader set of financial arrangements and revisit the debate on programmability in the design of monetary systems.

This talk presents a novel theory of choice centered around the idea that exploration is at the heart of human decision-making. Through interactions with their increasingly complex environment, people and organizations learn their capabilities, strengths, and limitations. Exploration can be incentivized in two distinct ways. The first one is an innate propensity for exploration (PEX), which makes exploration intrinsically rewarding. Curiosity and boredom, evident in humans as well as animals, are emotions facilitating such rewards. An alternative approach for incentivizing exploration is the adoption of biased perceptions of reality. Unlike PEX, this approach is flexible as people, organizations, and societies can fine-tune their beliefs to achieve desired levels of exploration. Thus, biased perceptions serve the important purpose of fine-tuning exploratory behavior when inherited incentives are sub-optimal.

Numerous behavioral patterns that appear irrational at first glance, such as context-dependent choice, self-sabotage and even superstitions, could be rationalized as effective mechanisms guiding individual exploration. Exploration also provides an explanation of economic (hyper) activity. Entrepreneurs start new businesses not only because they believe they have a great idea but also because they would like to explore their abilities to develop and commercialize new ideas. Managers experiment with a new product line not only because they believe it would be profitable, but also because they would like to discover the relative advantages of their enterprise. We are designed to constantly explore an unknown and vastly mysterious world and, in the process, discover and develop who we are.

Some of these ideas are developed in the book “A Theory of Dynamic Preferences: Using Economics and Behavioral Sciences to Explore Bias, Irrationality, and Choice” published by Palgrave Macmillan, a part of Springer Nature.

An opportunity to reflect on the day and to discuss plans for the future.