Effective strategies for targeted attacks to the network of Cosa Nostra affiliates

Network dismantling has recently gained interest in the fields of intelligence agencies, anti-corruption analysts and criminal investigators due to its efficiency in disrupting the activity of malicious agents. Here, we apply this approach to detect effective strategies for targeted attacks to Cosa Nostra by analysing the collaboration network of affiliates that participate to the same crimes. We preliminarily detect statistically significant homophily patterns induced by being member of the same mafia syndicate. We also find that links between members belonging to different mafia syndicates play a crucial role in connecting the network into a unique component, confirming the relevance of weak ties. Inspired by this result we investigate the resilience properties of the network under random and targeted attacks with a percolation based toy model. Random removal of nodes results to be quite inefficient in dismantling the network. Conversely, targeted attacks where nodes are removed according to ranked network centralities are significantly more effective. A strategy based on a removal of nodes that takes into account how much a member collaborates with different mafia syndicates has an efficiency similar to the one where nodes are removed according to their degree. The advantage of such a strategy is that it does not require a complete knowledge of the underlying network to be operationally effective

The interpretations and uses of fitness landscapes in the social sciences

__Abstract__ This working paper precedes our full article entitled “The evolution of Wright’s (1932) adaptive field to contemporary interpretations and uses of fitness landscapes in the social sciences” as published in the journal Biology & Philosophy (http://link.springer.com/article/10.1007/s10539-014-9450-2). The working paper features an extended literature overview of the ways in which fitness landscapes have been interpreted and used in the social sciences, for which there was not enough space in the full article. The article features an in-depth philosophical discussion about the added value of the various ways in which fitness landscapes are used in the social sciences. This discussion is absent in the current working paper. Th

Malicious Digital Penetration of United States Weaponized Military Unmanned Aerial Vehicle Systems: A National Security Perspective Concerning the Complexity of Military UAVs and Hacking

The United States’ (US) military unmanned aerial vehicle (UAV) has seen increased usage under the post 9/11 military engagements in the Middle East, Afghanistan, and within American borders. However, the very digital networks controlling these aircrafts are now enduring malicious intrusions (hacking) by America’s enemies. . The digital intrusions serve as a presage over the very digital networks the US relies upon to safeguard its national security and interests and domestic territory. The complexity surrounding the hacking of US military UAVs appears to be increasing, given the advancements in digital networks and the seemingly inauspicious nature of artificial intelligence and autonomous systems. Being most victimized by malicious digital intrusions, the US continues its military components towards growing dependence upon digital networks in advancing warfare and national security and interests. Thus, America’s netcentric warfare perspectives may perpetuate a chaotic environment where the use of military force is the sole means of safeguarding its digital networks

Semantic and Syntactic Transfer of Fitness Landscape Models to the Analysis of Collective and Public Decision-Making Processes

We set out to develop a method and research technique that could unite both modelling and case-based observations in order to analyse collective decision-making processes. Following Abbott’s (2001) recommendation regarding social processes, we have defined collective decision-making as an uninterrupted and non-directional process that is structured in sequences or lineages of events. To structure these processes, we re-modelled the basic components of Kauffman’s (1993) NK-model. We converted N to ‘problem and solution definitions’ (PSDs) and K to ‘connectedness’ between actors (c_score). An important modification is that we consider NK to be a dependent configuration; i.e., K entails both content and process. Fitness is defined as the probability of an actor achieving (elements of) its PSD as a result of its adaptive moves in relation to the adaptive moves of others. The model is put to the test in four different studies: (1) 20 years of decision-making in planning, building and servicing HSL-Zuid high-speed railways in the Netherlands; (2) the strategic search process of villages and cantons in the Gotthard region of Switzerland; (3) the redevelopment of a football stadium and the surrounding area in south Rotterdam, the Netherlands; and (4) the rise and fall of the Airport RailLink in Bangkok, Thailand. From these studies, we derived six archetypes in collection decision-making, subdivided into actor archetypes and interaction archetypes. For the actor archetypes, behavioural consistency is not just a trait for the actor but also affects the space of possibilities and/or behaviours of other actors. The interactions of individual actors combine to produce self-propagating dynamics that drive the further evolution of the collective decision-making process. The fitness field model enables researchers to investigate the various dimensions of the collective decision-making process – ranging from individual strategies and actions to variation, selection and retention of contents, from interactions to fitness gains and losses, and back again

ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM

Il terrorismo di stampo jihadista rappresenta una minaccia per la società e una sfida per gli scienziati interessati a comprenderne la complessità. Questa complessità richiede costantemente nuovi sviluppi in termini di ricerca sul terrorismo. Migliorare la conoscenza empirica rispetto a tale fenomeno può potenzialmente contribuire a sviluppare applicazioni concrete e, in ultima istanza, a prevenire danni all’uomo. In considerazione di tali aspetti, questa tesi presenta un nuovo quadro metodologico che integra scienza delle reti, modelli stocastici e apprendimento profondo per far luce sul terrorismo jihadista sia a livello esplicativo che predittivo. In particolare, questo lavoro compara e analizza le organizzazioni jihadiste più attive a livello mondiale (ovvero lo Stato Islamico, i Talebani, Al Qaeda, Boko Haram e Al Shabaab) per studiarne i pattern comportamentali e predirne le future azioni. Attraverso un impianto teorico che si poggia sulla concentrazione spaziale del crimine e sulle prospettive strategiche del comportamento terroristico, questa tesi persegue tre obiettivi collegati utilizzando altrettante tecniche ibride. In primo luogo, verrà esplorata la complessità operativa delle organizzazioni jihadiste attraverso l’analisi di matrici stocastiche di transizione e verrà presentato un nuovo coefficiente, denominato “Normalized Transition Similarity”, che misura la somiglianza fra paia di gruppi in termini di dinamiche operative. In secondo luogo, i processi stocastici di Hawkes aiuteranno a testare la presenza di meccanismi di dipendenza temporale all’interno delle più comuni sotto-sequenze strategiche di ciascun gruppo. Infine, il framework integrerà la meta-reti complesse e l’apprendimento profondo per classificare e prevedere i target a maggiore rischio di essere colpiti dalle organizzazioni jihadiste durante i loro futuri attacchi. Per quanto riguarda i risultati, le matrici stocastiche di transizione mostrano che i gruppi terroristici possiedono un ricco e complesso repertorio di combinazioni in termini di armi e obiettivi. Inoltre, i processi di Hawkes indicano la presenza di diffusa self-excitability nelle sequenze di eventi. Infine, i modelli predittivi che sfruttano la flessibilità delle serie temporali derivanti da grafi dinamici e le reti neurali Long Short-Term Memory forniscono risultati promettenti rispetto ai target più a rischio. Nel complesso, questo lavoro ambisce a dimostrare come connessioni astratte e nascoste fra eventi possano essere fondamentali nel rivelare le meccaniche del comportamento jihadista e come processi memory-like (ovvero molteplici comportamenti ricorrenti, interconnessi e non randomici) possano risultare estremamente utili nel comprendere le modalità attraverso cui tali organizzazioni operano.Jihadist terrorism represents a global threat for societies and a challenge for scientists interested in understanding its complexity. This complexity continuously calls for developments in terrorism research. Enhancing the empirical knowledge on the phenomenon can potentially contribute to developing concrete real-world applications and, ultimately, to the prevention of societal damages. In light of these aspects, this work presents a novel methodological framework that integrates network science, mathematical modeling, and deep learning to shed light on jihadism, both at the explanatory and predictive levels. Specifically, this dissertation will compare and analyze the world's most active jihadist terrorist organizations (i.e. The Islamic State, the Taliban, Al Qaeda, Boko Haram, and Al Shabaab) to investigate their behavioral patterns and forecast their future actions. Building upon a theoretical framework that relies on the spatial concentration of terrorist violence and the strategic perspective of terrorist behavior, this dissertation will pursue three linked tasks, employing as many hybrid techniques. Firstly, explore the operational complexity of jihadist organizations using stochastic transition matrices and present Normalized Transition Similarity, a novel coefficient of pairwise similarity in terms of strategic behavior. Secondly, investigate the presence of time-dependent dynamics in attack sequences using Hawkes point processes. Thirdly, integrate complex meta-networks and deep learning to rank and forecast most probable future targets attacked by the jihadist groups. Concerning the results, stochastic transition matrices show that terrorist groups possess a complex repertoire of combinations in the use of weapons and targets. Furthermore, Hawkes models indicate the diffused presence of self-excitability in attack sequences. Finally, forecasting models that exploit the flexibility of graph-derived time series and Long Short-Term Memory networks provide promising results in terms of correct predictions of most likely terrorist targets. Overall, this research seeks to reveal how hidden abstract connections between events can be exploited to unveil jihadist mechanics and how memory-like processes (i.e. multiple non-random parallel and interconnected recurrent behaviors) might illuminate the way in which these groups act

The co-evolution of networked terrorism and information technology

This thesis describes for the first time the mechanism by which high-performing terrorist networks leverage new iterations of information technology and the two interact in a mutually propulsive manner. Using process tracing as its methodology and complexity theory as its ontology, it identifies both terrorism and information technology as complex adaptive systems, a key characteristic of whose make-up is that they co-evolve in pursuit of augmented performance. It identifies this co-evolutionary mechanism as a classic information system that computes the additional scale with which the new technology imbues its terrorist partner, in other words, the force multiplier effect it enables. The thesis tests the mechanism’s theoretical application rigorously in three case studies spanning a period of more than a quarter of a century: Hezbollah and its migration from terrestrial to satellite broadcasting, Al Qaeda and its leveraging of the internet, and Islamic State and its rapid adoption of social media. It employs the NATO Allied Joint Doctrine for Intelligence Procedures estimative probability standard to link its assessment of causal inference directly to the data. Following the logic of complexity theory, it contends that a more twenty-first century interpretation of the key insight of RAND researchers in 1972 would be not that ‘terrorism evolves’ but that it co-evolves, and that co-evolution too is arguably the first logical explanation of the much-vaunted ‘symbiotic relationship’ between terrorists and the media that has been at the heart of the sub-discipline of terrorism studies for 50 years. It maintains that an understanding of terrorism based on co-evolution belatedly explains the newness of much-debated ‘new terrorism’. Looking forward, it follows the trajectory of terrorism driven by information technology and examines the degree to which the gradual symbiosis between biological and digital information, and the acknowledgment of human beings as reprogrammable information systems, is transforming the threat landscape

Networking- Eine meta-analytische Untersuchung

Networking stellt eine wichtige Karriereselbstmanagement-Strategie (Sturges et al., 2010) sowie Kompetenz (Akkermans et al., 2012) dar. Im Rahmen narrativer Reviews wurde das Forschungsfeld bereits strukturiert und erste theoretische Rahmenmodelle entwickelt (Gibson et al., 2014; Wolff et al., 2008). Daran anknüpfend werden in der vorliegenden Arbeit die drei am häufigsten untersuchten Determinanten, worunter Persönlichkeitseigenschaften, Alter sowie Geschlecht zu verstehen sind, meta-analytisch untersucht. Mithilfe der Methode der Meta-Analyse ist es möglich die Erkenntnisse aus den narrativen Reviews quantitativ zu überprüfen und auch kleine Effekte valide zu bestimmen. Die vorliegende Arbeit basiert auf einer extensiven systematischen Literaturrecherche und im Rahmen der Untersuchung konnte festgestellt werden, dass eine Proaktive Persönlichkeit und Self-Monitoring die stärksten Zusammenhänge mit Networking aufweisen. Alter und Geschlecht stehen hingegen in keinem Zusammenhang mit allgemeinem Networking-Verhalten. Durch eine spezifische Betrachtung des Networking-Konstrukts auf Facetten-Ebene zeigten sich jedoch relevante Befunde. So hängt das Alter negativ mit der Nutzung von Networking-Kontakten zusammen. Geschlechterunterschiede konnten hinsichtlich dem Aufbau und der Pflege von Networking-Kontakten konstatiert werden, da Männer dem häufiger nachkommen als Frauen. Für die Pflege von Networking-Kontakten ist es von Bedeutung, ob die Networking-Kontakte innerhalb oder außerhalb der eigenen Organisation tätig sind, sodass hier eine noch differenziertere Berücksichtigung der Networking-Facetten angezeigt ist. Die Befunde führen neben theoretischen Implikationen auch zu praktischen Implikationen hinsichtlich Trainings und Personalentscheidungen. In zukünftiger Forschung sollte vor allem die Rolle funktioneller Networking-Facetten näher beleuchtet werden, da diese in der bisherigen Literatur vernachlässigt wurden