Global economic activity is surrounded by increasing uncertainties from various sources. In this paper, we focus on commodity prices and estimate a global commodity uncertainty factor by capturing comovement in volatilities of major agricultural, metals and energy commodity markets through a group-specific Dynamic Factor Model. Then, by computing impulse response functions estimated using a small-scale Structural VAR model, we find that an increase in the common commodity price uncertainty results in a substantial and persistent drop in investment and trade, for a set of emerging and advanced economies. We also show that a global commodity uncertainty shock is more detrimental for shortand long-term economic growth than usual financial and economic policy uncertainty shocks. Last, our methodology turns out to be an efficient way to disentangle "good" and "bad" macroeconomic effects of oil price uncertainty: when an oil price uncertainty shock is common to all commodities, then the macroeconomic effect is likely to be negative, similar to a global demand shock. However, when the uncertainty shock is only specific to the oil market, the short-run effect tends to be positive.
This paper investigates how, and under what conditions, generative artificial intelligence (AI) systems, particularly fine-tuned large language models (LLMs), stimulate cognitive flexibility and enhance the flexibility pathway to creativity. Drawing on the dual pathway to creativity model, we examine the role of task breadth in facilitating creative idea generation. Through two randomized experimental studies, we found that fine-tuned LLM-generated stimuli significantly enhance breadth of exploration, which is a consequence of cognitive flexibility, when participants engage in exploration within a broad domain (Study 1). In contrast, no significant effect is observed within a constrained domain (Study 2). These findings highlight the critical interaction between generative AI and task structure in driving creativity. Our study contributes to a deeper understanding of how AI systems reshape cognitive processes, providing theoretical and practical insights into the evolving nature of creativity in the era of generative AI.
Classical Cellular Automata (CCAs) are a powerful computational framework for modeling global spatio-temporal dynamics with local interactions. While CCAs have been applied across numerous scientific fields, identifying the local rule that governs observed dynamics remains a challenging task. Moreover, the underlying assumption of deterministic cell states often limits the applicability of CCAs to systems characterized by inherent uncertainty. This study, therefore, focuses on the identification of Cellular Automata on spaces of probability measures (CAMs), where cell states are represented by probability distributions. This framework enables the modeling of systems with probabilistic uncertainty and spatially varying dynamics. Moreover, we formulate the local rule identification problem as a parameter estimation problem and propose a meta-heuristic search based on Self-adaptive Differential Evolution (SaDE) to estimate local rule parameters accurately from the observed data. The efficacy of the proposed approach is demonstrated through local rule identification in two-dimensional CAMs with varying neighborhood types and radii.
Purpose The aim of this study is to analyze the impact of real estate risks on the dynamics of financial sector stock returns, using a sample of countries across Asia and Oceania, Europe and North America, from January 2000 to December 2024, a period marked by significant crises including the Global Financial Crisis and the COVID-19 pandemic. Design/methodology/approach The wavelet quantile correlation (WQC) is implemented to shed new light on the dynamic real estate risk exposure in the financial sector (including the risk frequency, scale and location) during periods of turmoil and exuberance. With this metric, we can also analyze tail dependence and explore its consequences and implications across different investment horizons, for investors, real estate developers, bankers, policymakers and other stakeholders. Considering the connectedness between the real estate and financial sectors, we propose two factors to measure real estate risk along two complementary dimensions: U.S. real estate risk and domestic real estate risk. Findings Based on the WQC metric, our results separately report U.S. and domestic real estate risk exposures, which significantly affect global financial sector returns across three investment horizons: short-term (4–8 months), mid-term (32–64 months) and long-term (64–128 months). Globally, real estate risk exposure increases with investment horizon, while tail risk declines. Domestic real estate risk exposure is prevalent and higher in Asia, Europe and North America for all investment horizons. As revealed by the quantile analysis, U.S. real estate risk exposure in the financial sector is more significant in U.S. and European countries, whereas Asian countries (particularly Japan) are more affected by domestic real estate risk exposure. Originality/value We develop an international analysis of real estate risk exposure in the financial sector for a sample of 14 countries. We consider two complementary dimensions: U.S. real estate risk and domestic real estate risk, and we use a recently developed econometric methodology, based on WQC. Our results highlight the relative importance of real estate risk exposures in the global financial sector. These findings may inform the calculation of risk-weighted assets by banks, as required under the new 2025 Basel IV framework.
Organizations need cultures that support their work-sometimes this requires the culture to change. However, culture is often experienced as "invisible." Enterprise social media might enable culture change by tracing activity (posts, group formation, comments etc.), thereby making culture visible. Yet, this visibility raises a compelling puzzle: How do leaders and followers use the cultural visibility that social media offers during change? We find that because enterprise social media makes culture visible, it makes it easy for leaders to attend to big-C changes in the culture while overlooking small-c reactions, which are also visible, that undo the change. In the end, the very thing that makes social media a possible solution for cultural change also hampers leaders from sustaining the change: visibility becomes a liability.
