Banned from the sharing economy: an agent-based model of a peer-to-peer marketplace for consumer goods and services.

The emergence of profit-based online platforms related to the Sharing Economy, such as BlaBlaCar and Airbnb, provides new means for end users to create an income from their possessions. With this opportunity, participants have to make strategic economic decisions despite limited formal expertise and information. Decentralization (using digital technologies) and reputation (using user reviews) are the core mechanisms chosen by these platforms to mitigate these limitations and to work efficiently as online matchmakers. We test the performance of these two mechanisms by studying the allocative efficiency (in terms of value and volume of transactions) of simulated marketplaces under different types of motivation from the participants and control from the platforms. As a result, we find an inverted-U relationship between the decision-making leeway available to the participants and the platform's allocative efficiency. From the participants' perspectives, too much freedom or too many barriers lead to market failures affecting specific participants: low-end consumers are banned from the marketplace while high-end providers experience lower levels of activity. As governance advice for these platforms, we show the limitations of promoting these platforms on the sole motive of monetary rewards.

Evolving user needs and late-mover advantage.

We propose a generalized NK-model of late-mover advantage where late-mover firms leapfrog first-mover firms as user needs evolve over time. First movers face severe trade-offs between the provision of functionalities in which their products already excel and the additional functionalities requested by users later on. Late movers, by contrast, start searching when more functionalities are already known and typically come up with superior product designs. We also show that late-mover advantage is more probable for more complex technologies. Managerial implications follow.

Complexity and demographic explanations of cumulative culture.

Formal models have linked prehistoric and historical instances of technological change (e.g., the Upper Paleolithic transition, cultural loss in Holocene Tasmania, scientific progress since the late nineteenth century) to demographic change. According to these models, cumulation of technological complexity is inhibited by decreasing--while favoured by increasing--population levels. Here we show that these findings are contingent on how complexity is defined: demography plays a much more limited role in sustaining cumulative culture in case formal models deploy Herbert Simon's definition of complexity rather than the particular definitions of complexity hitherto assumed. Given that currently available empirical evidence doesn't afford discriminating proper from improper definitions of complexity, our robustness analyses put into question the force of recent demographic explanations of particular episodes of cultural change.