28
F. Maggino et al.
Bruggemann, R., & Patil, G. P. (2011). Ranking and prioritization for multi-indicator systems –
Introduction to partial order applications. New York: Springer.
Bruggemann, R., & Voigt, K. (2011). A new tool to analyze partially ordered sets – Application:
Ranking of polychlorinated biphenyls and alkanes/alkenes in river main, Germany. MATCH
Communications in Mathematical and in Computer Chemistry, 66, 231–251.
Bruggemann, R., & Voigt, K. (2012). Antichains in partial order, example: Pollution in a German
region by Lead, Cadmium, Zinc and Sulfur in the herb layer. MATCH Communications in
Mathematical and in Computer Chemistry, 67, 731–744.
Bruggemann, R., Sørensen, P. B., Lerche, D., & Carlsen, L. (2004). Estimation of averaged ranks
by a local partial order model. Journal of Chemical Information and Computer Sciences, 44,
618–625.
Bubley, R., & Dyer, M. (1999). Faster random generation of linear extensions. Discrete Mathematics, 201, 81–88.
Carlsen, L. (2018). Happiness as a sustainability factor. The world happiness index: A poseticbased data analysis. Sustainability Science, 13, 549–571.
Carlsen, L., & Bruggemann, R. (2017). Fragile state index: Trends and developments, a partial
order data analysis. Social Indicators Research, 133, 1–14.
Davey, B. A., & Priestley, H. A. (1990). Introduction to lattices and order. Cambridge: Cambridge
University Press.
De Loof, K., De Meyer, K. H., & De Baets, B. (2006). Exploiting the lattice of ideals representation
of a poset. Fundamenta Informaticae, 71, 309–321.
Edwards, J. R. (2011). The fallacy of formative measurement. Organizational Research Methods,
14(2), 370–388.
Erdi, P. (2008). Complexity explained. Berlin, Heidelberg: Springer. https://doi.org/10.1007/9783-540-35778-0.
Fattore, M. (2016). Partially ordered sets and the measurement of multidimensional ordinal
deprivation. Social Indicators Research, 128, 835–858.
Fattore, M., & Maggino, F. (2014). Partial orders in socio-economics. A practical challenge for
poset theorists or a cultural challenge for social scientists? In R. Bruggemann, L. Carlsen, & J.
Wittmann (Eds.), Multi-indicator systems and modelling in partial order (pp. 197–214). New
York: Springer.
Fattore, M., Maggino, F., & Greselin, F. (2011). Socio-economic evaluation with ordinal variables:
Integrating and poset approaches. Statistica & Applicazioni, Special Issue, 2011, 31–42.
Gorban, A. N., & Yablonsky, G. S. (2013). Grasping complexity. Computers & Mathematics with
Applications, 65(10), 1421–1426.
Kerber, A. (2017). Evaluation, considered as problem orientable mathematics over lattices. In R.
Bruggemann & M. Fattore (Eds.), Partial order concepts in applied sciences (pp. 87–103).
Cham: Springer.
Kerber, A., & Bruggemann, R. (2015). Problem driven evaluation of chemical compounds and
its exploration. MATCH Communications in Mathematical and in Computer Chemistry, 73,
577–613.
Lazarsfeld, P. F. (1958). Evidence and inference in social research. Daedalus, 87(4), 99–130.
Lima, M. (2013). Visual complexity: Mapping patterns of information. New York: Princeton
Architectural Press.
Luther, B., Bruggemann, R., & Pudenz, S. (2000). An approach to combine cluster analysis with
order theoretical tools in problems of environmental pollution. Match, 42, 119–143.
Maggino, F. (2009). The state of the art in indicators construction in the perspective of a
comprehensive approach in measuring Well-being of societies. Firenze: Firenze University
Press, Archivio E-Prints.
Maggino, F. (2017a). Developing indicators and managing the complexity. In F. Maggino (Ed.),
Complexity in society: From indicators construction to their synthesis (pp. 87–114). Cham:
Springer.
F. Maggino et al.
Bruggemann, R., & Patil, G. P. (2011). Ranking and prioritization for multi-indicator systems –
Introduction to partial order applications. New York: Springer.
Bruggemann, R., & Voigt, K. (2011). A new tool to analyze partially ordered sets – Application:
Ranking of polychlorinated biphenyls and alkanes/alkenes in river main, Germany. MATCH
Communications in Mathematical and in Computer Chemistry, 66, 231–251.
Bruggemann, R., & Voigt, K. (2012). Antichains in partial order, example: Pollution in a German
region by Lead, Cadmium, Zinc and Sulfur in the herb layer. MATCH Communications in
Mathematical and in Computer Chemistry, 67, 731–744.
Bruggemann, R., Sørensen, P. B., Lerche, D., & Carlsen, L. (2004). Estimation of averaged ranks
by a local partial order model. Journal of Chemical Information and Computer Sciences, 44,
618–625.
Bubley, R., & Dyer, M. (1999). Faster random generation of linear extensions. Discrete Mathematics, 201, 81–88.
Carlsen, L. (2018). Happiness as a sustainability factor. The world happiness index: A poseticbased data analysis. Sustainability Science, 13, 549–571.
Carlsen, L., & Bruggemann, R. (2017). Fragile state index: Trends and developments, a partial
order data analysis. Social Indicators Research, 133, 1–14.
Davey, B. A., & Priestley, H. A. (1990). Introduction to lattices and order. Cambridge: Cambridge
University Press.
De Loof, K., De Meyer, K. H., & De Baets, B. (2006). Exploiting the lattice of ideals representation
of a poset. Fundamenta Informaticae, 71, 309–321.
Edwards, J. R. (2011). The fallacy of formative measurement. Organizational Research Methods,
14(2), 370–388.
Erdi, P. (2008). Complexity explained. Berlin, Heidelberg: Springer. https://doi.org/10.1007/9783-540-35778-0.
Fattore, M. (2016). Partially ordered sets and the measurement of multidimensional ordinal
deprivation. Social Indicators Research, 128, 835–858.
Fattore, M., & Maggino, F. (2014). Partial orders in socio-economics. A practical challenge for
poset theorists or a cultural challenge for social scientists? In R. Bruggemann, L. Carlsen, & J.
Wittmann (Eds.), Multi-indicator systems and modelling in partial order (pp. 197–214). New
York: Springer.
Fattore, M., Maggino, F., & Greselin, F. (2011). Socio-economic evaluation with ordinal variables:
Integrating and poset approaches. Statistica & Applicazioni, Special Issue, 2011, 31–42.
Gorban, A. N., & Yablonsky, G. S. (2013). Grasping complexity. Computers & Mathematics with
Applications, 65(10), 1421–1426.
Kerber, A. (2017). Evaluation, considered as problem orientable mathematics over lattices. In R.
Bruggemann & M. Fattore (Eds.), Partial order concepts in applied sciences (pp. 87–103).
Cham: Springer.
Kerber, A., & Bruggemann, R. (2015). Problem driven evaluation of chemical compounds and
its exploration. MATCH Communications in Mathematical and in Computer Chemistry, 73,
577–613.
Lazarsfeld, P. F. (1958). Evidence and inference in social research. Daedalus, 87(4), 99–130.
Lima, M. (2013). Visual complexity: Mapping patterns of information. New York: Princeton
Architectural Press.
Luther, B., Bruggemann, R., & Pudenz, S. (2000). An approach to combine cluster analysis with
order theoretical tools in problems of environmental pollution. Match, 42, 119–143.
Maggino, F. (2009). The state of the art in indicators construction in the perspective of a
comprehensive approach in measuring Well-being of societies. Firenze: Firenze University
Press, Archivio E-Prints.
Maggino, F. (2017a). Developing indicators and managing the complexity. In F. Maggino (Ed.),
Complexity in society: From indicators construction to their synthesis (pp. 87–114). Cham:
Springer.