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Civil wars are as frequent and debilitating now as ever. More often than not, their resolution consists of the negotiation of a peace accord that involves a number of provisions. Although previous work in political science indicates an underlying interdependence between provision implementation sequences, it is unclear how the structure and dynamics of this interdependence relate to the successful implementation of peace accords. To fill this gap, we systematically study peace processimplementationactivityfrom34peaceaccordscontaining 51 provisions negotiated between 1989 and 2015. We begin by constructing a bipartite network between peace accords and their provisions implementation and explore statistical properties of the structural underpinnings of peace processes. Then, we examine motifs (i.e., significantly frequent patterns) in provision implementation activity and uncover higher order correlations between provisions.Finally, we identify provision implementation sequences (i.e., meta-groups) that are most strongly associated with successful peace processes. Our empirical findings provide new insights for the implementation of peace accords by revealing temporal sequences of peace process implementation that help build confidence, enhance security, and ultimately prevent negative cascading effects in different stages of the peacebuilding process. 

Sharing economy platforms are rapidly scaling up by reaching increasingly diverse demographics. However, this expansion comes with great difficulties in adequately identifying and responding to everyone’s needs. In this paper, we study gender-related behaviors of guests on the currently most prominent home-sharing platform, Airbnb. While our results confirm the efficacy of Airbnb’s reputation system, we also find that the level of trust and participation on the platform varies by gender. In particular, female solo travelers are more likely to be conscious of review sentiment and choose more often female hosts than male solo travelers. Our findings are obtained by combining exploratory data analysis with large-scale experiment and call for further studies on the usage of sharing economy platforms among subpopulations, informing and improving both policy and practice in these growing online environments. 

With digital music consumption being at an all-time high, online music encyclopedia like MusicBrainz and music intelligence platforms like The Echo Nest are becoming increasingly important in identifying, organizing, and recommending music for listeners around the globe. As a byproduct, such sites collect comprehensive information about a vast amount of artists, their recorded songs, institutional support, and the collaborations between them. Using a unique mash-up of crowdsourced, curated, and algorithmically augmented data, this paper unpacks an unsolved problem that is key to promoting artistic innovation, i.e., how gender penetrates into artistic context leading to the globally perceived gender gap in the music industry. Specifically, we investigate gender-related differences in the sonic features of artists’ work, artists’ tagging by listeners, their record label affiliations, and collaboration networks. We find statistically significant disparities along all these dimensions. Moreover, the differences allow models to reliably identify the gender of songs’ creators and help elucidate the role of cultural and structural factors in sustaining inequality. Our findings contribute to a better understanding of gender differences in music production and inspire strategies that could improve the recognition of female artists and advance gender equity in artistic leadership and innovation. 

Crowd financing is a burgeoning phenomenon that promises to improve access to capital by enabling borrowers with limited financial opportunities to receive small contributions from individual lenders towards unsecured loan requests. Faced with information asymmetry about borrowers' credibility, individual lenders bear the entire loss in case of loan default. Predicting loan payment is therefore crucial for lenders and for the sustainability of these platforms. To this end, we examine whether the ''wisdom'' of the lending crowd can provide reliable decision support with respect to projects' long-term success. Using data from Prosper.com, we investigate the association between the dynamics of lending behaviour and successful loan payment through interpretable classification models. We find evidence for collective intelligence signals in lending behaviour and observe variability in crowd wisdom across loan categories. We find that the wisdom of the lending crowd is most prominent in the auto loan category, but it is statistically significant for all other categories except student debt. Our study contributes new insights on how signals deduced from lending behaviour can improve the efficiency of crowd financing thereby contributing to economic growth and societal development. 

Crowdfunding platforms promise to disrupt investing as they bypass traditional financial institutions through peer-to-peer transactions. To stay functional, these platforms require a supply of investors who are willing to contribute to campaigns. Yet, little is known about the retention of investors in this setting. Using four years of data from a leading equity crowdfunding platform, we empirically study the length and success of investor activity on the platform. We analyze temporal variations in these outcomes and explain patterns using statistical modeling. Our models are based on information about user's past and current investment decisions, i.e., content-based and structural similarities between the campaigns they invest in. We uncover the role of past successes and diversity of investment decisions for novice vs. serial investors. Our results inform potential strategies for increasing the retention of investors and improving their decisions on crowdfunding platforms.