Saving Human Lives: What Complexity Science and Information Systems can Contribute

Helbing, Dirk; Brockmann, Dirk; Chadefaux, Thomas; Donnay, Karsten; Blanke, Ulf; Woolley-Meza, Olivia; Moussaïd, Mehdi; Johansson, Anders; Krause, Jens; Schütte, Sebastian; Perc, Matjaž

doi:10.1007/s10955-014-1024-9

Cited by 496 publications

(275 citation statements)

References 134 publications

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“…Crime itself is a complex phenomenon with unexpected social behaviours being seen which are difficult to understand, control and, sometimes, even to quantify (D'Orsogna and Perc, 2015;Helbing et al, 2015). For instance, it is natural to assume that by enforcing longer prison sentences, harder punishments or by increasing fines, less crime would be observed, but this is not usually true (Becker, 1968).…”

Section: Introductionmentioning

confidence: 99%

Fear of crime: the impact of different distributions of victimisation

Curiel

bishop

2018

Palgrave Commun

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There is often a mismatch between levels of crime and the fear of becoming a victim of crime. It is not uncommon to find individuals who suffer little or no crime but yet who are still fearful of some future crime. Alternatively, a place or region might see an increase in crime over time while the fear of crime remains unchanged. Building on a model that previously considered the fear of crime as an opinion shared by simulated individuals, here the impact that different distributions of crime have on the fear experienced by the population is analysed. Simulating the dynamics of the fear of individuals, along with changes of the distribution of crime, leads to results which show that fear is sensitive to the distribution of crime and that there is a phase transition for high levels of concentration of crime. A policy may be oriented to reduce crime, so that the population effectively suffers less crime, but if the victimisation is displaced to other individuals, then the perception of insecurity may not decrease, with fear becoming more widespread.

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Section: Introductionmentioning

confidence: 99%

Fear of crime: the impact of different distributions of victimisation

Curiel

bishop

2018

Palgrave Commun

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“…In each case, knowledge about the diverse contributing factors is dispersed. For these highdimensional problems, it is becoming impossible for any single individual or agency to gather and process enough data to understand the entire system (16). In many cases, we do not even have full knowledge of what the potential causal factors are, let alone a full understanding of them.…”

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confidence: 99%

Optimal incentives for collective intelligence

Mann

Helbing

2017

Proc. Natl. Acad. Sci. U.S.A.

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Collective intelligence is the ability of a group to perform more effectively than any individual alone. Diversity among group members is a key condition for the emergence of collective intelligence, but maintaining diversity is challenging in the face of social pressure to imitate one's peers. Through an evolutionary game-theoretic model of collective prediction, we investigate the role that incentives may play in maintaining useful diversity. We show that market-based incentive systems produce herding effects, reduce information available to the group, and restrain collective intelligence. Therefore, we propose an incentive scheme that rewards accurate minority predictions and show that this produces optimal diversity and collective predictive accuracy. We conclude that real world systems should reward those who have shown accuracy when the majority opinion has been in error.collective intelligence | game theory | democracy | diversity | markets T he financial crisis and its aftermath have reopened longstanding debates about the collective wisdom of our societal organizations (1-3). Financial and prediction markets seem unable to foresee major economic and political upheavals, such as the credit crunch or Brexit. This lack of collective foresight could be the result of insufficient diversity among decisionmaking individuals (4). Diversity has been identified as a key ingredient of successful groups across many facets of collective behavior (5-7). It is a crucial condition for collective intelligence (6-10) that can be more important than the intelligence of individuals within a group (11). Because collective behavior ultimately results from individual actions, incentives play a major role for diversity and collective performance (12, 13). Although most previous research has focused on explaining how collective intelligence emerges (14), less is known about how to optimize the wisdom of crowds in a quantitative sense.Harnessing collective wisdom is important. Global systems of communication, governance, trade, and transport grow rapidly in complexity every year. Many of these real world problems have a large number of contributing factors. For example, predicting future economic fluctuations requires integrating knowledge about credit markets and supply chains across the world as well as the ramifications of political developments in different countries and the shifting sentiments of individual investors and consumers. Political developments are themselves the result of many factors: both direct (e.g., political parties' strategies) and indirect (e.g., technological change). Scientific questions are also increasingly complex. For instance, building a complete model of an ecosystem requires bringing together expertise on many scales from individual animal behavior to complex networks of predation and codependency (15). In each case, knowledge about the diverse contributing factors is dispersed. For these highdimensional problems, it is becoming impossible for any single individual or agency to gather and process...

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“…The investigated problems are becoming too complex to stay within the scope of a single discipline, and hence, interdisciplinary research is becoming more popular (Helbing et al, 2015). Hybrid societies are an inherently interdisciplinary problem domain, thus the inclusion of findings from various disciplines is essential for their structural and algorithmic design [e.g., combination of results from plant science, robotics, and architecture (Hamann et al, 2015)].…”

Section: Primary Challenge C: Interdisciplinarity In Hybrid Society Rmentioning

confidence: 99%

“…It is a hybrid system because the robot-animal interaction is not only in one way but also the animals determine the system's further development. Similarly, we have hybrid societies in socio-technical systems where human beings closely interact with technological artifacts (Baxter and Sommerville, 2010;Smirnov et al, 2014;D'Orsogna and Perc, 2015;Helbing et al, 2015).…”

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confidence: 99%

Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems

et al. 2016

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Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area.

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Saving Human Lives: What Complexity Science and Information Systems can Contribute

Cited by 496 publications

References 134 publications

Fear of crime: the impact of different distributions of victimisation

Fear of crime: the impact of different distributions of victimisation

Optimal incentives for collective intelligence

Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems

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