Unemployment as a social cost

Over the last decade, workfare programmes provided support to the unemployed only insofar as they were willing to accept a job. The theoretical underpinnings of these programmes are that institutional constraints prevent labour supply from adjusting to the technologically determined requirements of labour demand. We contend that when individuals look for a job, they generally want to take into account non-monetary features such as occupational status. Status cannot be traded, it usually is complementary to income, it determines lifestyles and life possibilities. As for labour demand, its requirements do not reflect efficient behaviour and technical constraints because business ”efficiency” cannot be taken to be a measure of social efficiency and technology cannot be used as a benchmark to assess the efficiency of business conduct. We suggest that Sen’s notion of capabilities may constitute an appropriate benchmark to assess the social efficiency of the economic system. This leads us to a few policy implications. The ”capabilities benchmark” leads us to stress the importance of freedom to choose how to conduct one’s life. Acting in favour of freedom involves the understanding of how business strategies affect learning patterns and available choice sets. It also involves the assessment of policy issues - such as cooperation between the scientific community and business, scientific freedom, educational goals and their institutional implementation, and unemployment relief systems - which may influence the relation between business strategies and social learning.

Introduzione all’archeologia del paesaggio. Geografia cosmica, simulazioni geomatiche, ricostruzioni potenziali e ipersuperfici neurali

L’archeologia ha da sempre descritto, analizzato e discusso la spazialità del ritrovamento ma, come per la datazione, anche la collocazione geografica di un documento, il suo posizionamento topografico e la sua rappresentazione carografica sono tecniche destinate ad esplorare il territorio, tecniche che sono cambiate nel tempo, in rapporto al trasformarsi delle percezioni spaziali (dei paesaggi, dei luoghi, dei “non”-luoghi), e con la conquista di altri punti di vista introdotti dalle relazioni interdisciplinari che lo studio del passato ha tracciato con la geomatica e le neuroscienze, relazioni capaci di potenziare la nostra percezione del micro e del macro-cosmo. Nel riconoscere alcuni lineamenti di queste trasformazioni potremmo risa- lire molto indietro nel tempo, ma sarebbe poi artificioso tentare di restituirli tutti ad una sequenza diacronica e rischiare di segnarli come “tratti” di un processo scientifico evolutivo, universale, ortogenetico. E allora cercheremo di procedere discutendo alcune delle diverse concezioni spaziali, come se queste fossero paradigmi complementari di lettura e misurazione della terra e degli uomini, paradigmi pluristratificati che affiorano sino all’età contemporanea e che stanno formando l’epistemologia stessa di quella che oggi, traducendo la definizione inglese di Landscape Archaeology, definiamo come Archeologia del Paesaggio. Questa nuova disciplina delle scienze storiche e archeologiche do- cumenta e analizza la complessa fenomenologia del rapporto tra ambiente, territorio e mobilità umana

Archeologia e traduzione. Prolegomena alla meccanografia e alla simulazione artificiale del sema

La traduzione di un dato archeologico in unità significante può apparire un processo tautologico, meccanico, ma essa riflette invece un percorso di cono- scenza, vincolato alla scelta di parametri analitici. In quanto processo cognitivo, questa traduzione avviene anche secondo principi di logica che tendono a or- ganizzare, in forma quanto più razionale, la documentazione raccolta; un dato diviene così il sema di una struttura più coerente il cui valore espressivo potrà essere rilevato tramite le dottrine dei segni. D’altronde, il record archeologico rientra sempre in un processo comunicativo, prima di essere un reperto, un documento o una testimonianza è solo un segno, tra gli infiniti, dello scambio informativo, e il suo luogo di ritrovamento come il testo, deflagrato, di un linguaggio estinto. Il presente contributo, dedi- cato al tema interdisciplinare della traduzione, sarà dunque un ulteriore tentativo di storicizzare la recente applicazione di modelli della biologia 17 computazionale alla ricostruzione e all’analisi dei fenomeni naturali e culturali complessi. Tali fenomeni sono qui intesi – essenzialmente – come segmenti della realtà empirica commutabile in codici alfanumerici; matrici, dunque, di un sistema che potrà essere interrogato

The Ebla Archaeological Park. Natural, Archaeological and Artificial Italian Portrait of the Ancient Syrian Capital

The paper focused on three interacted topics. At least will be critically discussed the methodological guidelines of the Ebla Archaeological Park that were listed in Paris during the Third ICAANE; second will be presented some of the archaeological discoveries strictly related to those methodological guidelines; third will be presented some of the last archaeological monuments and landscapes visual transformations of Tell Mardikh strictly related to the archaeological and conservation works activities

A Model of Colonic Crypts using SBML Spatial

The Spatial Processes package enables an explicit definition of a spatial environment on top of the normal dynamic modeling SBML capabilities. The possibility of an explicit representation of spatial dynamics increases the representation power of SBML. In this work we used those new SBML features to define an extensive model of colonic crypts composed of the main cellular types (from stem cells to fully differentiated cells), alongside their spatial dynamics.Comment: In Proceedings Wivace 2013, arXiv:1309.712

The Ideological and Aesthetic Relationship Between Ur And Ebla During The Third Millennium B. C

The paper focus on the main economic differences related to secondary urbanism processes in Lower Mesopotamia and Northern Syria and will compare some of the extraordinary art masterpieces (collected during excavations) from the Early Dynastic and Early Syrian periods, such as composite works that adopted more ancient aesthetic concepts. The economic difference between these two urbanisms and the technological analogy between their official figurative representations will reveal that the two worlds had similar aesthetic concepts which came from the Sumerian habitus. This was not only linguistically ‘ideographic’, but also cognitive and political

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018 International Conference on Computational Science

Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models

The emergence and development of cancer is a consequence of the accumulation over time of genomic mutations involving a specific set of genes, which provides the cancer clones with a functional selective advantage. In this work, we model the order of accumulation of such mutations during the progression, which eventually leads to the disease, by means of probabilistic graphic models, i.e., Bayesian Networks (BNs). We investigate how to perform the task of learning the structure of such BNs, according to experimental evidence, adopting a global optimization meta-heuristics. In particular, in this work we rely on Genetic Algorithms, and to strongly reduce the execution time of the inference -- which can also involve multiple repetitions to collect statistically significant assessments of the data -- we distribute the calculations using both multi-threading and a multi-node architecture. The results show that our approach is characterized by good accuracy and specificity; we also demonstrate its feasibility, thanks to a 84x reduction of the overall execution time with respect to a traditional sequential implementation

Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks

Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common examples of such diseases, where the mutational load in the cancerous/viral population increases over time. In these cases, selective pressures are often observed along with competition, cooperation and parasitism among distinct cellular clones. Recently, we presented a mathematical framework to model these phenomena, based on a combination of Bayesian inference and Suppes' theory of probabilistic causation, depicted in graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). SBCNs are generative probabilistic graphical models that recapitulate the potential ordering of accumulation of such DNA changes during the progression of the disease. Such models can be inferred from data by exploiting likelihood-based model-selection strategies with regularization. In this paper we discuss the theoretical foundations of our approach and we investigate in depth the influence on the model-selection task of: (i) the poset based on Suppes' theory and (ii) different regularization strategies. Furthermore, we provide an example of application of our framework to HIV genetic data highlighting the valuable insights provided by the inferred