Agentinio modeliavimo taikymas kultūros tyrimų informacijos valdymui

The impact of cultural processes on personal and social changes is one of the important research issues not only in contemporary social sciences but also for simulation of future development scenarios and evidence-based policy decision making. In the context of the theoretical concept of cultural values, based on the system theory and theory of social capital, the impact of cultural events could be analyzed and simulated by focussing on the construction/deconstruction of social capital, which takes place throughout the actor’s cultural participation. The main goal of this research is the development of measuring metrics, and agent-based simulation model aimed at investigation of the social impact of cultural processes.  This paper provides new insights of modeling the social capital changes in a society and its groups, depending on cultural participation. The proposed measurement metrics provide the measurement facility of three key components: actors, cultural events and events flow and social capital. It provides the initial proof of concept simulation results, - simplified agent-based simulation model showcase. The NetLogo MAS platform is used as a simulation environment.  Kultūros procesų poveikis asmens ir visuomenės pokyčiams yra viena svarbių šiuolaikinių tyrimų temų ne tik socialiniuose moksluose, tačiau ir modeliuojant visuomenės raidos scenarijus ar priimant argumentais grįstus politinio pobūdžio sprendimus. Remiantis sistemų teorija grindžiamu kultūros socialinio kapitalo verčių teoriniu konceptu, kultūros įvykių poveikis gali būti analizuojamas ir imitaciškai modeliuojamas koncentruojantis į socialinio kapitalo verčių didėjimą / mažėjimą, vykstantį asmeniui dalyvaujant kultūroje. Tyrimo tikslas – apibrėžti dalyvavimo kultūroje poveikio socialiniam kapitalui matavimo metriką ir matematiškai pagrįsti metodą (modelį), įgalinantį tirti kultūros procesų socialinį poveikį. Straipsnyje pristatomi šio tyrimo rezultatai: dalyvavimo kultūroje poveikio socialiniam kapitalui matavimo metrika (kultūros proceso dalyvių, kultūros įvykių, socialinio kapitalo matavimai), kultūros poveikio socialiniam kapitalui imitacinis modelis ir eksperimentiniai šio modelio taikymo rezultatai. Pasiūlyta metrika yra skirta pamatuoti trims pagrindiniams proceso komponentams: veikėjams (kultūros įvykių dalyviams), kultūros įvykiams ir jų srautui bei socialiniam kapitalui. Straipsnyje taip pat pateikiamas „NetLogo MAS“ simuliacinėje aplinkoje įgyvendintas supaprastintas agentinis modelis, skirtas populiacijos socialinio kapitalo dinamikos modeliavimui

Indoor-Guided Navigation for People Who Are Blind: Crowdsourcing for Route Mapping and Assistance

This paper presents an approach to enhance electronic traveling aids (ETAs) for people who are blind and severely visually impaired (BSVI) using indoor orientation and guided navigation by employing social outsourcing of indoor route mapping and assistance processes. This type of approach is necessary because GPS does not work well, and infrastructural investments are absent or too costly to install for indoor navigation. Our approach proposes the prior outsourcing of vision-based recordings of indoor routes from an online network of seeing volunteers, who gather and constantly update a web cloud database of indoor routes using specialized sensory equipment and web services. Computational intelligence-based algorithms process sensory data and prepare them for BSVI usage. In this way, people who are BSVI can obtain ready-to-use access to the indoor routes database. This type of service has not previously been offered in such a setting. Specialized wearable sensory ETA equipment, depth cameras, smartphones, computer vision algorithms, tactile and audio interfaces, and computational intelligence algorithms are employed for that matter. The integration of semantic data of points of interest (such as stairs, doors, WC, entrances/exits) and evacuation schemes could make the proposed approach even more attractive to BVSI users. Presented approach crowdsources volunteers’ real-time online help for complex navigational situations using a mobile app, a live video stream from BSVI wearable cameras, and digitalized maps of buildings’ evacuation schemes

Analysis of the financial markets dynamics using modern artificial intelligence methods

A review of financial capital markets, presented in the article, helped to reveal the extended use of modem information technologies and artificial intelligence methods in today’s financial markets. Artificial intelligence systems (AIS) embrace artificial neural networks, chaos theory, fractal theory, fuzzy logic, and genetic algorithm’s methods. Those theories and methods are well suited for the modeling of non linear dynamics and are capable to overtake other techniques in short term forecasting, trend prognosis, recognition of structural shifts, nonlinear correlations, and chaotic behavior. AIS are capable to coup with the modem financial markets problems, which more resemble adaptive, chaotic and evolutionary then static or equilibrium nature.The authors have stressed on the description of drawbacks of the traditional capital investment theories, formulation of theoretical and practical premises for the nonlinear approaches using modem information technologies like distributed databases, world-wide communication channels, parallel processing, and OLAP systems. Extended review of a related literature, helped to create the overall research scheme, based on AIS methods, which has mutually bounded and consistent research stages. The overall research scheme embraces: 1) research and description, using chaos and fractal methods, of the nonlinearities and their dynamics in financial time series; 2) creation of non linear complex models for SE indices approximation and prognosis using artificial neural network’s methods; 3) application of the different AIS methods like a consistent analytical tool for the bank clients crediting risk decision support model creation. The scheme made possible systematic review of various analytical possibilities for the different AIS methods. It also helped to create compound models for the analysis and prognosis of the dynamics in the financial markets.Straipsnyje nagrinėjamos finansinio kapitalo sektoriaus veiklos analizės ir prognozės problemos ir siūlomi efektyvūs jų sprendimo būdai. Parodoma, kad tradicinė finansinio kapitalo rinkų kitimo analizė, besiremianti tiesine paradigma, jau nėra efektyvi kriziniais ir pereinamaisiais (nepusiausviriais) laikotarpiais, kai vyrauja chaotiškoms sistemoms būdingi neperiodiniai svyravimai. Nagrinėjama, kaip šių problemų optimaliam sprendimui siūloma pritaikyti dirbtinio intelekto sistemų metodus, kurie šiuolaikinių informacinių technologijų dėka išplečia tradicinės analizės galimybes. Pasiūlyta bendra tyrimų schema, susidedanti į nuoseklių ir metodiškai pagrįstų tyrimo etapų pradedant tyrimo objekto dinamikos analize ir baigiant jo elgesį prognozuojančių modelių sudarymu ir tyrimu

Tiesioginių užsienio investicijų Rytų ir Vidurio Europoje analizė naudojant dirbtinius neuroninius tinklus

Central and Eastern European (CEE) countries are in an economic transition process which involves convergence of economic performance with the European Union. One of the principle engines for the necessary transformation towards EU average economic performance is inward-FDI. Quantitatively examining the causes of FDI in the CEE region is thus an important research area. Traditional linear regression approaches have had difficulty in achieving conceptually and statistically reliable results. In this paper, we offer a novel approach to examining FDI in the CEE region. The key tasks addressed in this research are (i) a neural network based FDI forecasting model and (H) nonlinear evaluation of the determinants of FDI. The methodology is non traditional for this kind of research (compared with multiple linear regression estimates) and is applied primarily for the FDI dynamics in the CEE region with some worldwide comparisons. In terms of MSE and Rsquared criteria, we find that NN approaches are better to explain FDI determinants’ weights than traditional regression methodologies. Our findings are preliminary but offer important and novel implications for future research in this area, including more detailed comparisons across sectors as well as countries over time.Vidurio ir Rytų Europos (YRE) šalys išgyvena pereinamuosius ekonominius procesus, kurie skatina ekonominę konvergenciją su Europos Sąjunga. Tiesioginės užsienio investicijos (TUI) yra vienas iš pagrindinių veiksnių, todėl TUI srautų analizė yra svarbi tyrimo sritis. Tradicinės daugiakriterinės tiesinės analizės priemonės nei konceptualiai, nei statistiškai neduoda patikimų rezultatų esant mažoms, ne visoms ir chaotiškoms duomenų sekoms. Šiame tyrime pateikiamas netradicinis (netiesinis), todėl naujoviškas požiūris į TUI VRE pavyzdžiu. Pagrindiniai iškelti uždaviniai: (a) TUI prognozavimo modelio sukūrimas naudojant dirbtinių neuroninių tinklų (DNT) metodus, (b) TUI turinčių įtakos veiksnių (makroekonominių, finansinių, socialinių ir gravitacinių) svorių įvertinimas DNT pagalba. Atlikti empiriniai tyrimai leido sukurti DNT modelį, kuris duoda kur kas geresnius aproksimavimo ir neblogesnius prognozavimo rezultatus, palyginti su daugiakriteriniu tiesiniu modeliu. Nors gauti rezultatai yra preliminarūs ir reikia išsamesnės analizės, tačiau jie siūlo svarbias ir naujoviškas įžvalgas būsimiems netiesiniams tyrimams, apimantiems detalius VRE sektorinius ir šalių tarpusavio palyginimus

Neural Network Approaches to Estimating FDI Flows: Evidence from Central and Eastern Europe

Central and East European (CEE) countries are in an economic transition process that involves convergence of their economic performance with the European Union. One of the principal engines for the necessary transformation toward EU average economic performance is inward foreign direct investment (FDI). Quantitatively examining the causes of FDI in the CEE region is thus an important research area. Traditional linear regression approaches have had difficulty achieving conceptually and statistically reliable results. In this paper, we offer a novel approach to examining FDI in the CEE region. The key tasks addressed in this research are a neural network (NN)âbased FDI forecasting model and a nonlinear evaluation of the determinants of FDI. The methodology is nontraditional for this kind of research (compared with multiple linear regression estimates) and is applied primarily for the FDI dynamics in the CEE region, with some worldwide comparisons. In terms of mean square error (MSE) and >i>R>/i>>sup>>i>2>/i>>/sup> criteria, we find that NN approaches better explain FDI determinants' weights than do traditional regression methodologies. Our findings are preliminary, but offer important and novel implications for future research in this area, including more detailed comparisons across sectors, as well as countries over time.