Synergy of Effectuation and Causation: An Emotional Complexity Perspective

This study enriches the literature on entrepreneurial decisions by investigating the antecedents of the synergetic use of causal and effectual logic. Based on entrepreneurial metacognition and emotional complexity theories, we argued that the emotional complexity of an entrepreneur, referred to as the granular experience of, or variety in, experienced emotions during the entrepreneurial task, would contribute to the synergetic use of decision logic. With survey data gathered from 218 Chinese entrepreneurs, we found that entrepreneurs with higher emotional complexity are more likely to adopt two types of entrepreneurial logic in tandem, and cognitive flexibility mediates this positive relationship. Thereby, this study helps to unravel some of the complexities behind the choice of decision logic of entrepreneurs.

Closing the Social Distance

The following paper is centered around the potential for organizational change in response to the COVID-19 pandemic. This paper argues that the disruption of “business as usual” during the COVID-19 pandemic provides opportunities to both highlight gendered organizational practices during remote work and explore how organizational actors might contribute to a more equitable restructuring of gendered communication practices once employees return to in-person work. First, the paper contextualizes the COVID-19 pandemic at the time of writing. Next, the literature review examines the notion of organizations as inherently gendered, the history of organizational change from Lewinian Planned Change to models of non-linear change, and bureaucratic organizational structures using a feminist lens. The discussion section then argues that complexity theories offer significant opportunities for improvement due to the destabilization of current workplace practices. This argument is followed up by examples of how organizations can successfully engage complexity theories to reduce gender inequality in the post-pandemic world. The paper concludes that by emphasizing consensus and autonomy, improvements to network communication and the merging of public and private spheres should be the first steps towards the ultimate goal of reducing gender inequality through the deconstruction of bureaucracies.

A Markov model of urban evolution: Neighbourhood change as a complex process

This paper seeks to advance neighbourhood change research and complexity theories of cities by developing and exploring a Markov model of socio-spatial neighbourhood evolution in Toronto, Canada. First, we classify Toronto neighbourhoods into distinct groups using established geodemographic segmentation techniques, a relatively novel application in this geographic setting. Extending previous studies, we pursue a hierarchical approach to classifying neighbourhoods that situates many neighbourhood types within the city’s broader structure. Our hierarchical approach is able to incorporate a richer set of types than most past research and allows us to study how neighbourhoods’ positions within this hierarchy shape their trajectories of change. Second, we use Markov models to identify generative processes that produce patterns of change in the city’s distribution of neighbourhood types. Moreover, we add a spatial component to the Markov process to uncover the extent to which change in one type of neighbourhood depends on the character of nearby neighbourhoods. In contrast to the few studies that have explored Markov models in this research tradition, we validate the model’s predictive power. Third, we demonstrate how to use such models in theoretical scenarios considering the impact on the city’s predicted evolutionary trajectory when existing probabilities of neighbourhood transitions or distributions of neighbourhood types would hypothetically change. Markov models of transition patterns prove to be highly accurate in predicting the final distribution of neighbourhood types. Counterfactual scenarios empirically demonstrate urban complexity: small initial changes reverberate throughout the system, and unfold differently depending on their initial geographic distribution. These scenarios show the value of complexity as a framework for interpreting data and guiding scenario-based planning exercises.

Mythic Imagination Today

Mythic Imagination Today is an illustrated guide to the interpenetration of mythology and science throughout the ages. This publication brings alive our collective need for story to guide the rules, roles, and relationships of everyday life. Whereas mythology is born primarily of perception and imagination, science emerges from systematic observation and experimentation. Both disciplines arise from endless curiosity about the workings of the Universe combined with creative urges to transform inner and outer worlds. Both disciplines are located within open neural wiring that gives rise to uniquely human capacities for learning, memory, and metaphor. Explored are the origins of story within the social brain; mythmakers and myths from multiple cultures; and how contemporary sciences of chaos and complexity theories and fractal geometry dovetail with ancient wisdom. The ancient Greek myth of Psyche and Eros is unpacked in detail—origins of the very concepts of ‘psyche’ and ‘psychology’.

A Markov model of urban evolution: Neighbourhood change as a complex process

This paper seeks to advance neighbourhood change research and complexity theories of cities by developing and exploring a Markov model of socio-spatial neighbourhood evolution in Toronto, Canada. First, we classify Toronto neighbourhoods into distinct groups using established geodemographic segmentation techniques, a relatively novel application in this geographic setting. Extending previous studies, we pursue a hierarchical approach to classifying neighbourhoods that situates many neighbourhood types within the city’s broader structure. Our hierarchical approach is able to incorporate a richer set of types than most past research and allows us to study how neighbourhoods' positions within this hierarchy shape their trajectories of change. Second, we use Markov models to identify generative processes that produce patterns of change in the city’s distribution of neighbourhood types. Moreover, we add a spatial component to the Markov process to uncover the extent to which change in one type of neighbourhood depends on the character of nearby neighbourhoods. In contrast to the few studies that have explored Markov models in this research tradition, we validate the model's predictive power. Third, we demonstrate how to use such models in theoretical scenarios considering the impact on the city’s predicted evolutionary trajectory when existing probabilities of neighbourhood transitions or distributions of neighbourhood types would hypothetically change. Markov models of transition patterns prove to be highly accurate in predicting the final distribution of neighbourhood types. Counterfactual scenarios empirically demonstrate urban complexity: small initial changes reverberate throughout the system, and unfold differently depending on their initial geographic distribution. These scenarios show the value of complexity as a framework for interpreting data and guiding scenario-based planning exercises.