Статті в журналах: "Political modelling"

1

Sørensen, Jan Rose. "Applied political economic modelling." European Journal of Political Economy 6, no. 3 (December 1990): 442–44. http://dx.doi.org/10.1016/0176-2680(90)90073-r.

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2

Byers, David, James Davidson, and David Peel. "Modelling political popularity: a correction." Journal of the Royal Statistical Society: Series A (Statistics in Society) 165, no. 1 (February 2002): 187–89. http://dx.doi.org/10.1111/1467-985x.00677.

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3

Armborst, Andreas. "Modelling Terrorism and Political Violence." International Relations 24, no. 4 (December 2010): 414–32. http://dx.doi.org/10.1177/0047117810385779.

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4

Goel, Pankaj, and Amanpreet Singh Brar. "Structural equation modelling of political marketing strategies adopted by political parties." Journal of Management Research and Analysis 5, no. 3 (September 15, 2018): 319–26. http://dx.doi.org/10.18231/2394-2770.2018.0050.

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5

Zakaria. "Modelling Political Support among Women with Political Actors in Northern States." Journal of Social Sciences 8, no. 1 (January 1, 2012): 104–8. http://dx.doi.org/10.3844/jssp.2012.104.108.

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6

Potters, Jan, and Frans Van Winden. "Modelling political pressure as transmission of information." European Journal of Political Economy 6, no. 1 (August 1990): 61–88. http://dx.doi.org/10.1016/0176-2680(90)90036-i.

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7

León-Castro, Ernesto, Luis A. Perez-Arellano, Maricruz Olazabal-Lugo, and Jose M. Merigó. "Prioritized Induced Heavy Operators Applied to Political Modelling." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 04 (August 2021): 603–20. http://dx.doi.org/10.1142/s0218488521500264.

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This paper presents the prioritized induced heavy ordered weighted average (PIHOWA) operator. This operator combines an unbounded weighting vector, an induced vector and a prioritized vector and can be applied to the group decision-making process where the information provided by each decision maker does not have the same importance. An application of this operator is done in governmental transparency in Mexico based on the Open Government Metric (OGM). Among the main results it is possible to visualize how the relative importance of each component can generate important change in the top 10 ranking.

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8

Mostafa, Mohamed M. "Modelling nations' political risk via computational intelligence techniques." International Journal of Computational Intelligence Studies 4, no. 3/4 (2015): 243. http://dx.doi.org/10.1504/ijcistudies.2015.072872.

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9

Milosch, Tim. "Morocco: Modelling Stability in Turbulent Waters." Journal of Arts and Humanities 5, no. 12 (December 13, 2016): 66. http://dx.doi.org/10.18533/journal.v5i12.1049.

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<p>Morocco and Egypt both experienced similar socioeconomic challenges in the last decade, but the Moroccan monarchy has been able to address those challenges without prompting civil conflict or anti-government rebellions. This presents an interpretive problem for the political science literature that views socioeconomic trends as being primary indicators of political instability. This case study proposes a more nuanced, multidisciplinary approach to the analysis of Morocco’s political culture by mapping findings in historical and anthropological research on to a political process framework in order to explain the Moroccan regime’s stability in terms of its religious legitimacy. It concludes with an assessment of how this knowledge can be used by countries outside the Middle East North Africa region (MENA) to better partner with MENA countries in developing stable political cultures.</p>

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10

Mitra, Sovan. "Political risk modelling and measurement from stochastic volatility models." International Journal of Sustainable Economy 11, no. 2 (2019): 184. http://dx.doi.org/10.1504/ijse.2019.099064.

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11

Mitra, Sovan. "Political risk modelling and measurement from stochastic volatility models." International Journal of Sustainable Economy 11, no. 2 (2019): 184. http://dx.doi.org/10.1504/ijse.2019.10020049.

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12

Marangos, John. "A political economy methodology in modelling the transition process." Global Business and Economics Review 5, no. 2 (2003): 197. http://dx.doi.org/10.1504/gber.2003.006209.

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13

Stanziola, Javier. "Modelling the heritage world: Economic, legal and political considerations." International Journal of Heritage Studies 4, no. 3-4 (January 1998): 168–76. http://dx.doi.org/10.1080/13527259808722234.

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14

Borooah, Vani K., and Friedrich Schneider. "Introduction to the special issue on political-economic modelling." European Journal of Political Economy 7, no. 4 (November 1991): 435–37. http://dx.doi.org/10.1016/0176-2680(91)90030-7.

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15

Stimson, Robert John, and Tung-Kai Shyy. "And now for something different: modelling socio-political landscapes." Annals of Regional Science 50, no. 2 (May 25, 2012): 623–43. http://dx.doi.org/10.1007/s00168-012-0505-5.

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16

Fang, Anjie. "Analysing political events on Twitter." ACM SIGIR Forum 53, no. 1 (June 2019): 38–39. http://dx.doi.org/10.1145/3458537.3458542.

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Recently, political events, such as elections, have raised a lot of discussions on social media networks, in particular, Twitter. This brings new opportunities for social scientists to address social science tasks, such as understanding what communities said or identifying whether a community has an influence on another. However, identifying these communities and extracting what they said from social media data are challenging and non-trivial tasks. We aim to make progress towards understanding 'who' (i.e. communities) said 'what' (i.e. discussed topics) and 'when' (i.e. time) during political events on Twitter. While identifying the 'who' can benefit from Twitter user community classification approaches, 'what' they said and 'when' can be effectively addressed on Twitter by extracting their discussed topics using topic modelling approaches that also account for the importance of time on Twitter. To evaluate the quality of these topics, it is necessary to investigate how coherent these topics are to humans. Accordingly, we propose a series of approaches in this thesis. First, we investigate how to effectively evaluate the coherence of the topics generated using a topic modelling approach. The topic coherence metric evaluates the topical coherence by examining the semantic similarity among words in a topic. We argue that the semantic similarity of words in tweets can be effectively captured by using word embeddings trained using a Twitter background dataset. Through a user study, we demonstrate that our proposed word embedding-based topic coherence metric can assess the coherence of topics like humans [1, 2]. In addition, inspired by the precision at k metric, we propose to evaluate the coherence of a topic model (containing many topics) by averaging the top-ranked topics within the topic model [3]. Our proposed metrics can not only evaluate the coherence of topics and topic models, but also can help users to choose the most coherent topics. Second, we aim to extract topics with a high coherence from Twitter data. Such topics can be easily interpreted by humans and they can assist to examine 'what' has been discussed and 'when'. Indeed, we argue that topics can be discussed in different time periods (see [4]) and therefore can be effectively identified and distinguished by considering their time periods. Hence, we propose an effective time-sensitive topic modelling approach by integrating the time dimension of tweets (i.e. 'when') [5]. We show that the time dimension helps to generate topics with a high coherence. Hence, we argue that 'what' has been discussed and 'when' can be effectively addressed by our proposed time-sensitive topic modelling approach. Next, to identify 'who' participated in the topic discussions, we propose approaches to identify the community affiliations of Twitter users, including automatic ground-truth generation approaches and a user community classification approach. We show that the mentioned hashtags and entities in the users' tweets can indicate which community a Twitter user belongs to. Hence, we argue that they can be used to generate the ground-truth data for classifying users into communities. On the other hand, we argue that different communities favour different topic discussions and their community affiliations can be identified by leveraging the discussed topics. Accordingly, we propose a Topic-Based Naive Bayes (TBNB) classification approach to classify Twitter users based on their words and discussed topics [6]. We demonstrate that our TBNB classifier together with the ground-truth generation approaches can effectively identify the community affiliations of Twitter users. Finally, to show the generalisation of our approaches, we apply our approaches to analyse 3.6 million tweets related to US Election 2016 on Twitter [7]. We show that our TBNB approach can effectively identify the 'who', i.e. classify Twitter users into communities. To investigate 'what' these communities have discussed, we apply our time-sensitive topic modelling approach to extract coherent topics. We finally analyse the community-related topics evaluated and selected using our proposed topic coherence metrics. Overall, we contribute to provide effective approaches to assist social scientists towards analysing political events on Twitter. These approaches include topic coherence metrics, a time-sensitive topic modelling approach and approaches for classifying the community affiliations of Twitter users. Together they make progress to study and understand the connections and dynamics among communities on Twitter. Supervisors : Iadh Ounis, Craig Macdonald, Philip Habel The thesis is available at http://theses.gla.ac.uk/41135/

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17

Bellany, Ian. "Modelling War." Journal of Peace Research 36, no. 6 (November 1999): 729–39. http://dx.doi.org/10.1177/0022343399036006008.

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18

Petersen, A. C. "The practice of climate simulation and its social and political context." Netherlands Journal of Geosciences - Geologie en Mijnbouw 87, no. 3 (September 2008): 219–29. http://dx.doi.org/10.1017/s0016774600023313.

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AbstractThe practice of climate simulation takes place in a polarized social and political context. In this paper some methodological aspects of the practice of climate simulation are addressed and the potential value-ladenness of modelling assumptions is discussed. I claim that there is clearly a plurality of values guiding climate simulation efforts with climate scientists themselves also commonly holding different political views on the climate-change problem. There exist climate models of varying levels of concreteness and with different basic assumptions, and the modelling approaches behind these models are valued differently by different groups of climate scientists. The social and political context in which the climate modelling is done plays a role in these value judgements. In order to prevent one particular group of models from dominating the field for social and/or political reasons, the climate-modelling community should acknowledge the vital and necessary role of plurality in the practice of climate science and should stimulate reflection within this practice. Finally, while the IPCC partly addresses the issue by presenting model ensembles, the uncertainties in climate simulation should be better communicated to policy makers and politicians.

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19

Anderton, R., and M. Desai. "Modelling Manufacturing Imports." National Institute Economic Review 123 (February 1988): 80–86. http://dx.doi.org/10.1177/002795018812300107.

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It is universally acknowledged that imports in the UK economy have risen very rapidly; import penetration in manufacturing has risen from 26 per cent in 1980 to 34 per cent in 1986. It is felt that any reflationary programme will have little impact on domestic employment since much of the extra spending power will be spent on imports, especially imports of manufactured goods. This has led to the firm belief that a reflationary programme has to be directed towards activities which have a lower import content and hence a higher domestic output and employment multiplier. This was the belief that made infrastructural expenditure such a popular option in the political discussions before the 1987 election.

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20

Minnery, John R. "MODELLING COORDINATION." Australian Journal of Public Administration 47, no. 3 (September 1988): 253–62. http://dx.doi.org/10.1111/j.1467-8500.1988.tb01067.x.

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21

Orrequia-Barea, Aroa, and Encarnación Almazán Ruiz. "Modelling through Modality: (Re)shaping Brexit." ES Review. Spanish Journal of English Studies, no. 42 (November 9, 2021): 127–53. http://dx.doi.org/10.24197/ersjes.42.2021.127-153.

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Due to Brexit, the UK has been involved in a continuous political debate between Boris Johnson, the Prime Minister, and Jeremy Corbyn, the leader of the opposition. This paper compares and analyses the modality used in a corpus consisting of their political speeches until Brexit day. Modal verbs are used to express ability, possibility, willingness, certainty, obligation and necessity. Politicians’ choice of certain words can be a useful tool to affect voters’ decisions and modality is a resource which reinforces that influence. The findings show remarkable similarities between both politicians and reveal that possibility is the most frequent meaning of the modal verbs used in the corpus.

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22

Kushneruk, Svetlana L. "WORLD-MODELLING POTENTIAL OF THE DISCOURSE OF A POLITICAL EVENT." Bulletin of the South Ural State University series Linguistics 15, no. 3 (2018): 26–31. http://dx.doi.org/10.14529/ling180304.

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23

Langrock, Roland, Nils-Bastian Heidenreich, and Stefan Sperlich. "Kernel-based semiparametric multinomial logit modelling of political party preferences." Statistical Methods & Applications 23, no. 3 (April 2, 2014): 435–49. http://dx.doi.org/10.1007/s10260-014-0261-z.

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24

Schiavo, M. Lo. "The modelling of political dynamics by generalized kinetic (Boltzmann) models." Mathematical and Computer Modelling 37, no. 3-4 (March 2003): 261–81. http://dx.doi.org/10.1016/s0895-7177(03)00005-0.

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25

Sayer, Andrew. "Mathematical Modelling in Regional Science and Political Economy: Some Comments." Antipode 17, no. 2-3 (September 1985): 42–49. http://dx.doi.org/10.1111/j.1467-8330.1985.tb00332.x.

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26

Düring, Bertram, and Marie-Therese Wolfram. "Opinion dynamics: inhomogeneous Boltzmann-type equations modelling opinion leadership and political segregation." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2182 (October 2015): 20150345. http://dx.doi.org/10.1098/rspa.2015.0345.

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We propose and investigate different kinetic models for opinion formation, when the opinion formation process depends on an additional independent variable, e.g. a leadership or a spatial variable. More specifically, we consider (i) opinion dynamics under the effect of opinion leadership, where each individual is characterized not only by its opinion but also by another independent variable which quantifies leadership qualities; (ii) opinion dynamics modelling political segregation in ‘The Big Sort’, a phenomenon that US citizens increasingly prefer to live in neighbourhoods with politically like-minded individuals. Based on microscopic opinion consensus dynamics such models lead to inhomogeneous Boltzmann-type equations for the opinion distribution. We derive macroscopic Fokker–Planck-type equations in a quasi-invariant opinion limit and present results of numerical experiments.

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27

Laver, Michael, and Michel Schilperoord. "Spatial models of political competition with endogenous political parties." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1485 (April 11, 2007): 1711–21. http://dx.doi.org/10.1098/rstb.2007.2062.

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Two important human action selection processes are the choice by citizens of parties to support in elections and the choice by party leaders of policy ‘packages’ offered to citizens in order to attract this support. Having reviewed approaches analysing these choices and the reasons for doing this using the methodology of agent-based modelling, we extend a recent agent-based model of party competition to treat the number and identity of political parties as an output of, rather than an input to, the process of party competition. Party birth is modelled as an endogenous change of agent type from citizen to party leader, which requires describing citizen dissatisfaction with the history of the system. Endogenous birth and death of parties transforms into a dynamic system even in an environment where all agents have otherwise non-responsive adaptive rules. A key parameter is the survival threshold, with lower thresholds leaving citizens on average less dissatisfied. Paradoxically, the adaptive rule most successful for party leaders in winning votes makes citizens on average less happy than under other policy-selection rules.

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28

Rigoli, Francesco. "Political motivation: A referent evaluation mathematical model." Journal of Social and Political Psychology 9, no. 1 (February 19, 2021): 8–23. http://dx.doi.org/10.5964/jspp.5539.

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Mathematical modelling is popular in cognitive psychology because it enables clear and formal descriptions of the processes at play; yet, this approach has rarely been applied to political psychology. Here we adopt mathematical modelling to develop a theory of political motivation, which is a central concept in political psychology. The theory assumes that, in certain contexts, individuals entertain a set of representations of society, for example of the past, present and future (but also of fictive societies such as utopias). To each representation of society, an incentive value is attached which is not absolute, but (following theories of motivation in cognitive psychology) reference-dependent; namely, dependent on the context, corresponding to the whole set of representations of society. In turn, the model proposes that these subjective values determine two central aspects: a motivation for performing an appropriate political action and the ensuing political mood. We discuss the model with respect to theoretical and empirical research (and we examine Marx and Engel’s communist manifesto as an example of the latter). In short, we offer a new mathematical perspective on political motivation which emphasises the role of multiple representations of society in determining political motivation and the ensuing political mood.

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29

Poto, Margherita Paola, and Elise Johansen. "Modelling Ocean Connectivity." Arctic Review on Law and Politics 12 (2021): 186. http://dx.doi.org/10.23865/arctic.v12.3289.

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Regulatory coherence is crucial to effectively respond to the growing pressures that our oceans are facing. Applying the interpretative lens of ocean connectivity to ocean governance can help address the challenges from a material, epistemic, and geopolitical viewpoint. This special issue intends to uncover various understandings of ocean connectivity taking into account the complex biocultural interactions happening in the marine environment. The research aim is divided into two objectives: (1) to explore the various conceptualizations of ocean connectivity; and (2) to provide a critical analysis on how the law (of the sea) considers or disregards ocean connectivity. Our research methodology combines a literature review and a mapping technique that examines the models of connectivity. The mapping technique has been developed by adopting the ‘one-pager approach’, where the authors have been asked to answer two research questions, aligned with our research objectives. We structured the work into an introductory section and three main articles. The understanding of ocean connectivity is key to developing international marine policy and suggesting legal tools for the protection of the marine environment. Moving from this angle towards an understanding of connectivity which includes bio-centric elements, Indigenous cosmo-visions, and anthropocentric connectivity, we identified three models of connectivity and explored their suitability to address the systemic challenges.

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30

Martin-Nielsen, Janet. "Computing the Climate." Historical Studies in the Natural Sciences 48, no. 2 (April 1, 2018): 223–45. http://dx.doi.org/10.1525/hsns.2018.48.2.223.

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This paper traces the development of numerical climate models in the United Kingdom from 1963, when the U.K.’s Meteorological Office first took up climate modelling, to the mid-to-late 1970s, when climate change became politicized in the United Kingdom. The central question posed is how U.K. climate modellers developed rhetoric, managed expectations, and weighed their professional and political responsibilities in the face of growing political interest in climate change. Whilst the modellers were reluctant to allow the modelling results to be used for political ends, U.K. civil servants saw climate modelling as a modern tool for a new problem. As scientific and political agendas diverged, the director of the Meteorological Office, John Mason, found himself caught between his position as a government employee in a service organization and his responsibility as a gatekeeper between climate models and their potential uses. Ultimately, as Mason and his modellers were forced to admit, their climate models became cultural and political as well as scientific objects.

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31

Semenov, Andrei. "Time-series analysis in political sciences: opportunities and limitations." Political Science (RU), no. 1 (2021): 76–97. http://dx.doi.org/10.31249/poln/2021.01.03.

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In this paper, I consider opportunities and limitations of modelling the political dynamics with the time-series instruments. Using the examples of the president Putin’s approval rating and readiness to join the collective actions with economic demands I demonstrate the analytical potential of autoregressive integrated moving average model (ARIMA), autoregressive distributed lag model (ADL), and error correction model (ECM). Modelling the political dynamics faces a string of analytical dilemmas. This paper aims at identifying the basic choices in application of the statistical instruments to dynamic processes and helping the other researchers to navigate through them. While it is hard to account in a single paper for all the developments in the discipline, which has been substantially advanced substantially and technically i the last three decades, this text also aims at stimulating the discussion on the opportunities and limitations when applied to Russian politics.

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32

Eden, Colin, and Fran Ackermann. "Modelling Stakeholder Dynamics for Supporting Group Decision and Negotiation: Theory to Practice." Group Decision and Negotiation 30, no. 5 (May 28, 2021): 1001–25. http://dx.doi.org/10.1007/s10726-021-09745-y.

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AbstractGroup Decision and Negotiation methods can help identify optimal, or efficient, solutions to complex problems and so aid decision-makers. However, recommendations depend for their success, in part, on their political feasibility within, usually, complex organisational settings. A part of the complexity of effective implementation derives from understanding the responses of stakeholders to the proposed decisions. The responses of stakeholders can be complex because stakeholders respond not just to the decision but also to the responses of other stakeholders. When the recommendations are very important, and when the possible stakeholder responses are likely to be complex, then the use of some form of modelling of stakeholder dynamics is likely to be helpful. This paper proposes such a modelling process designed to aid the thinking of a decision-making team as they seek to ensure their decision is politically feasible. The modelling process is designed to facilitate effective negotiation in groups about the potential impact of stakeholder responses. The modelling process is illustrated through a real case.

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33

Erlingsson, Gissur O. "Modelling Secessions from Municipalities." Scandinavian Political Studies 28, no. 2 (June 2005): 141–59. http://dx.doi.org/10.1111/j.0080-6757.2005.00125.x.

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34

Garratt, Dean, and Joshy Easaw. "Externalities and the Political Business Cycle." Journal of Interdisciplinary Economics 7, no. 2 (April 1996): 127–41. http://dx.doi.org/10.1177/02601079x9600700203.

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Political business cycle theories in modelling the interaction between the polity and the economic system have continually failed to consider questions of economic and political interdependence between nations. In considering the current principal political business cycle models we ask how the reality of integrated economies affects the implications of these models. This is particularly important given the direction of the European Union (EU). The purpose here is to generate a series of hypotheses for future research and for empirical analysis and to show the short-comings of political business cycle models that are purely domestically-focused.

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35

Freeman, Rachel. "Modelling the socio-political feasibility of energy transition with system dynamics." Environmental Innovation and Societal Transitions 40 (September 2021): 486–500. http://dx.doi.org/10.1016/j.eist.2021.10.005.

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36

Jacobini, Otávio Roberto, and Maria Lúcia L. Wodewotzki. "Mathematical modelling: a path to political reflection in the mathematics class." Teaching Mathematics and its Applications: An International Journal of the IMA 25, no. 1 (March 1, 2006): 33–42. http://dx.doi.org/10.1093/teamat/hri026.

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37

Fielding, David. "Modelling Political Instability and Economic Performance: Israeli Investment during the Intifada." Economica 70, no. 277 (February 2003): 159–86. http://dx.doi.org/10.1111/1468-0335.t01-2-00276.

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38

Pettigrew, Thomas F. "Modern racial prejudice in America: Social psychological dimensions and political modelling." Patterns of Prejudice 22, no. 4 (December 1988): 3–12. http://dx.doi.org/10.1080/0031322x.1988.9969977.

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39

Sjøvaag, Helle, Truls André Pedersen, and Ole Martin Lægreid. "Journalism and the political structure." Nordicom Review 40, no. 2 (March 1, 2019): 63–89. http://dx.doi.org/10.2478/nor-2019-0034.

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Abstract This article assumes a media system perspective on the local news media structure in Norway, using a dataset of 847,487 news articles collected from 156 Norwegian news outlets in 2015–2017. Using a series of hypotheses, the analysis uses Latent Dirichlet Allocation (LDA) topic modelling to ascertain to what extent local journalism meets community information needs through infrastructure, output and performance. The analysis finds that the size of the publisher and the size of the community covered matter more for hard news coverage than regulatory factors. To that end, the results indicate that the Norwegian local media system is somehow shaped by the geography of the political landscape. The results and their contributions are discussed in light of media systems theory and local journalism structures.

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40

Dragozova-Ivanova, Elena, Ivan Paligorov, Ivaylo Ivanov, and Stanislava Kovacheva. "Management Modelling for Forest Landscapes." Economics and Business 28, no. 1 (April 1, 2016): 90–97. http://dx.doi.org/10.1515/eb-2016-0013.

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AbstractThe article presents alternatives for sustainable management of forest landscapes based on the analysis of Teteven area in Bulgaria. The conclusions are a result of research of 21 main factors, which are identified and evaluated with varying weight by experts and classified in five groups: environmental, social, economic, technological and political. In order to identify the most important and key factors, a structural analysis is applied. The alternatives are summarised in four scenarios called No Management, Ecological and Close to the Nature, Traditional Management and Maximum Potential Benefit.

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41

Jeffries, Ann. "Modelling Community Work:." Journal of Community Practice 3, no. 3-4 (December 16, 1996): 101–25. http://dx.doi.org/10.1300/j125v03n03_04.

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42

SZYMAŃSKA, Aleksandra, and Marc JEGERS. "MODELLING SOCIAL ENTERPRISES." Annals of Public and Cooperative Economics 87, no. 4 (February 8, 2016): 501–27. http://dx.doi.org/10.1111/apce.12127.

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43

Rolstadås, Asbjørn, and Per Morten Schiefloe. "Modelling project complexity." International Journal of Managing Projects in Business 10, no. 2 (April 4, 2017): 295–314. http://dx.doi.org/10.1108/ijmpb-02-2016-0015.

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Purpose The purpose of this paper is to enhance the understanding of what project complexity is, what drivers and factors that influence complexity and how consequences for organizational performance can be assessed. Design/methodology/approach The research is explanatory and based on literature review, model development, interviews and case studies. The model is validated through a case study. Findings The findings are a model for identifying and analyzing complexity drivers and complexity factors. The model starts with generic complexity drivers such as ambiguity, uncertainty, unpredictability and pace. These drivers are in each project influenced by nature and by socio-political, economic and technological surroundings to result in complexity factors that are specific to the project analyzed. The model can be used to analyze project complexity and to define requirements for the organization of the project and guidelines for the execution. Research limitations/implications The research is limited to large projects with a technical delivery of some kind of facilities. Practical implications The model can be used to assess the required capability of the organization for successful project execution. Originality/value The contribution of the research is a new model for understanding project complexity. The distinction between project complexity drivers and factors is essential as well as the taxonomy for the factors building on and adding to already published research.

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Mahmoud, Ali Bassam, Nicholas Grigoriou, Leonora Fuxman, and William D. Reisel. "Political advertising effectiveness in war-time Syria." Media, War & Conflict 13, no. 4 (April 23, 2019): 375–98. http://dx.doi.org/10.1177/1750635219841356.

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This study addresses the effectiveness of political advertising in an extreme context, during war-time. A self-administered cross-sectional survey was used to collect data during the 2016 parliamentary elections in Syria. Structural equation modelling was utilized to test the hypothetical model and its invariance related to political involvement. The results indicated that beliefs are a four-dimensional structure consisting of information, veracity, sarcasm, and cynicism. Furthermore, war-time perceptions were found to negatively affect attitude towards political advertising via sarcasm among less politically involved voters. Negative attitude was found to be linked to lower levels of veracity among such voters and to higher levels of cynicism for those who are highly involved in politics. Negative attitudes regarding political advertising were found for lowering the chances for watching advertisements, for supporting a candidate, and for willingness to vote. The results also revealed that paying attention to political advertising does not relate to voters’ intention to vote. This study is the first of its kind to empirically validate a conceptual model predicting voters’ turnout behaviour based on voters’ war-time perceptions, beliefs and attitudes regarding political advertising in an authoritarian setting. In addition, this study investigates whether the effects of the proposed model may be moderated by voters’ political involvement.

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Schoenberg, Ronald, Andreas Diekmann, and Peter Mitter. "Stochastic Modelling of Social Processes." Contemporary Sociology 15, no. 3 (May 1986): 449. http://dx.doi.org/10.2307/2070077.

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West, Marc, and Bernard Kachoyan. "Survival modelling of Australian politicians." Political Science 71, no. 2 (May 4, 2019): 140–57. http://dx.doi.org/10.1080/00323187.2019.1701948.

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Heath, Oliver. "Modelling the components of political action in Britain: The impact of civic skills, social capital and political support." British Elections & Parties Review 14, no. 1 (September 2004): 75–93. http://dx.doi.org/10.1080/1368988042000258781.

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Throsby, David. "Modelling the cultural industries." International Journal of Cultural Policy 14, no. 3 (August 2008): 217–32. http://dx.doi.org/10.1080/10286630802281772.

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Lyons, Anthony. "Modelling international conflict under conditions of state level economic and political uncertainty." Defence and Peace Economics 12, no. 6 (January 2001): 497–535. http://dx.doi.org/10.1080/10430710108405001.

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Johnson, Paul E. "Formal theories of politics: The scope of mathematical modelling in political science." Mathematical and Computer Modelling 12, no. 4-5 (1989): 397–404. http://dx.doi.org/10.1016/0895-7177(89)90412-3.

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