Building a global community to improve how complex real-world problems are tackled

Community Member Institutionalisation , , , , , , , , , , ,

Community member post by Gabriele Bammer

This is the third annual “state of the blog” review.

Gabriele Bammer (biography)

As the blog moves into its 4th year, how well is it achieving its goals? Is it succeeding in sharing concepts and methods across the multiple groups addressing complex real-world problems – groups including inter- and trans- disciplinarians, systems thinkers, action researchers and implementation scientists, as well as the myriad researchers working on complex environmental, health and other societal problems, who do not necessarily identify with these networks? Is it providing a forum to connect these disparate groups and individuals? Is it helping to build an international research community to improve how complex real-world problems are tackled?

In addition to addressing these questions, I list ten blog posts that you should not miss. That is followed by the most viewed blog posts of 2018, as well as the most viewed over the life of the blog.

I hope you’ll be enticed to read or revisit some of the rich array of posts available on the i2Insights blog.

Is the blog succeeding in its aims?

Sharing concepts and methods

Slowly but surely, we’re making progress. With 221 blog posts now published, there’s a growing library of useful concepts and methods for tackling complex societal and environmental problems. While these are the main focus of the blog, we also publish teaching practices, institutionalization examples and case studies.

By-and-large the posts have a long shelf life, so even those published when the blog first started are still useful and relevant. And we don’t just focus on publishing new concepts and methods. One of the challenges we’re seeking to overcome is that practices established in one community may be completely unknown in another, so we’re also keen to provide blog posts on the best existing tools, as well as new ones.

Blog posts now cover 250 topics (with several topics usually included in one blog post) and you can see the most popular in the topics cloud in the right hand column. The top ten (actually eleven) are:

Examples of ten topics that are less commonly covered but no less important are:

The easiest way to find blog posts relevant to your interests is to use the tags in the topics cloud or the search function. For example, if you want to know about integration, just click on the tag ‘integration’ and you’ll get a list of blog posts on that topic. If you want a less common topic, eg reflexivity, search for that term and four (currently) blog posts will be listed.

Alternatively if you want to scan the blog for tools or ideas that you might be unfamiliar with, browse the list of all posts. Or if you are not already one of the (almost) 900 subscribers to the blog, signing up will provide you with a weekly e-mail that alerts you to the most recent addition. We try to make the aim of the blog post clear in the title and opening sentences, so that you can quickly decide if it’s of interest.

As Tilo Weber’s blog post reminds us, language matters. We welcome blog posts in more than one language and we’ve had some success in providing blog posts in Spanish (seven posts), French (two posts) and Portuguese (one post), as well as English. We don’t have resources to provide other translations ourselves, and the best we can do at this stage is provide an easy link to Google Translate (near the top of the right hand column).

Building a community

We’re always keen to attract new authors, as well as to welcome back existing contributors. For the 2018 blog posts there were 59 new authors and 18 returning contributors.

Additionally, we want to expand the countries involved in the blog. The blog is now read in well over 90% of the 193 countries recognised by the United Nations. The number of views in 2018 ranged from less than 10 in 32 countries to between 1,000 and just over 20,000, in (in increasing numbers) India, the Netherlands, New Zealand, South Africa, Switzerland, Germany, Canada, the United Kingdom, Australia and the USA.

We’re keen to increase the number of countries our authors come from. We currently have contributors from 31 countries and we are delighted that four of these countries were added in 2018.

We’re particularly keen to make this a truly global forum. Complex societal and environmental problems are tackled in every country and improving the array of relevant concepts and methods is one area where each country has something to contribute.

If you haven’t written a blog post before we’ll support you. In fact, the majority of contributors were blog novices. We’ve had good feedback about the editing and other help we provide.

The opportunity to comment on blog posts and to exchange views is a critical part of the blog.  There was a significant increase in commenting between 2016 and 2017, with commenting falling back slightly in 2018 (although the year isn’t quite finished yet). The median number of comments was 2 in 2016, 7 in 2017 and 6 in 2018. The percentage of blog posts with no comments was 26% in 2016, 6% in 2017 and 8% in 2018.

Overall we are moving forward in building a larger community to connect members of existing groups working on complex real-world problems, as well as teams and individual researchers who are not members of the transdisciplinary, systems thinking or other networks. But there’s still much more that can be done – and we’d be thrilled if you were involved!

Final words for 2018

We’re taking a two-week break. We already have some great blog posts lined up for 2019 and the first will appear in the week of January 7. In the meantime we’d be delighted to hear from you. Do provide your thoughts and feedback in the comments section below. Do send in your ideas for blog posts. And last but not least, happy reading!

Ten blog posts you should not miss 

  1. Find out about argument-based tools that can be used to systematize decision making if key information is missing or contested, or when probabilities or values are undetermined:  Argument-based tools to account for uncertainty in policy analysis and decision support by Sven Ove Hansson and Gertrude Hirsch Hadorn
  2. See how the outcome measures used in evaluation of services can be improved by involving service users in their construction: Overturning the design of outcome measures by Diana Rose
  3. What are the tangible physical entities (time, funding, space etc) required for effective transdisciplinary research?: Material resources for transdisciplinary research by Chris Riedy
  4. Discover useful practices for cross-cultural research: Cross-cultural collaborative research: A reflection from New Zealand by Jeff Foote
  5. See how to use art for effective community engagement: Art and participatory modelling by Hara Woltz and Eleanor Sterling
  6. Learn what’s needed for effective collaboration by funders, researchers and end-users: Designing applied research for impact by Andrew Campbell
  7. Find out how to expand and institutionalise successful complex initiatives: Scaling up amidst complexity by Ann Larson
  8. How can you identify behaviours that block change?: Bringing the Immunity-to-ChangeTM process to the scientific community by Erica Lawlor and Cheryl Vaughan
  9. Discover how to take stock of all the evidence on a complex issue and how to use risk as the integrative framework: Using the concept of risk for transdisciplinary assessment by Greg Schreiner
  10. Learn how to reduce value conflict by sharing stories and distilling commonalities: Getting to a shared definition of a “good” solution in collaborative problem-solving by Doug Easterling

Most viewed of the 2018 blog posts (more than 600 views)

Inter- and trans- disciplinarity and team science

Research implementation

Education

Most viewed blog posts over the life of the blog (more than 1000 views)

Inter- and trans- disciplinarity

Research implementation

Education

Co-Creation

Unknowns

Practical guidance and specific methods

Biography: Gabriele Bammer PhD is a professor at The Australian National University in the Research School of Population Health’s National Centre for Epidemiology and Population Health. She is developing the new discipline of Integration and Implementation Sciences (i2S) to improve research strengths for tackling complex real-world problems through synthesis of disciplinary and stakeholder knowledge, understanding and managing diverse unknowns, and providing integrated research support for policy and practice change. 

Conceptual modelling of complex topics: ConML as an example / Modelado conceptual de temas complejos: ConML como ejemplo

Community Member Methods , , , , , , , ,

Community member post by Cesar Gonzalez-Perez

cesar-gonzalez-perez
Cesar Gonzalez-Perez (biography)

A Spanish version of this post is available

What are conceptual models? How can conceptual modelling effectively represent complex topics and assist communication among people from different backgrounds and disciplines?

This blog post describes ConML, which stands for “Conceptual Modelling Language”. ConML is a specific modelling language that was designed to allow researchers who are not expert in information technologies to create and develop their own conceptual models. It is useful for the humanities, social sciences and experimental sciences.

What are conceptual models?

A conceptual model is a formal or semi-formal representation of a topic under investigation, using concepts rather than physical parts. Conceptual models are generally visualised in the form of diagrams plus accompanying text, as shown in the figure below.

A modelling language is an artificial language designed to express models. Since models are usually depicted in the form of diagrams for convenience, modelling languages often incorporate a graphical notation. Like natural languages, modelling languages have:

  • a lexicon, that is, a set of the “words” that exist in the language
  • a syntax, that is, a set of the rules that tell us how we can combine those words in order to compose meaningful “sentences”
  • a semantics, that is, a description of the relationship between each “word” in the lexicon and those things in the world for which it stands.

The “words” of modelling languages are not conveyed through text or sound like those of natural languages, but usually through icons and drawings to help the formal visualisation of the abstract concepts. The syntactic rules of the modelling language tell us how these icons can be connected together to express models, and what each kind of connection means.

ConML contains very few “words”, and its syntax is very simple.

Conceptual modelling was developed within software engineering, and it has been applied to a number of domains beyond that, including business organisation, genomics or archaeology. ConML tries to introduce know-how and techniques that are usually only available to software engineers to specialists in other areas, including the humanities and social sciences.

conML_cesar-gonzalez-perez_english-image
Sample ConML diagram showing that complete archaeological objects are often fragmented, and what subtypes exist regarding origin and intentionality. (Source: Gonzalez-Perez 2018).

How can conceptual modelling effectively represent complex topics and assist communication among people from different backgrounds and disciplines?

There are two key issues:

  1. a conceptual model helps you better understand the portion of reality that you are dealing with, since it removes some of detail and complexity that often makes it unmanageable.
  2. a conceptual model constitutes a language in which to communicate statements about that portion of reality, especially when people from different backgrounds and disciplines are involved.

ConML aims to allow complex ideas to be communicated in a simple, meaningful way.

ConML recognises that different people and groups have different subjective views on things, entities change over time, and understandings are not always certain. This means that the information captured in a conceptual model might be, to varying extents, subjective, temporary and uncertain. ConML incorporates support for subjectivity and temporality, as well as basic support for uncertainty.

By incorporating the ability to express subjectivity, temporality and vagueness in models – thus addressing the crucial issues of multivocal, diachronic and imprecise or uncertain knowledge – ConML includes features that are usually absent from other modelling languages, and that make it especially suitable for the humanities and social sciences.

An example

A major application of ConML has been in building CHARM, a Cultural Heritage Abstract Reference Model.

CHARM represents things in terms of concepts, properties of concepts, and relationships between concepts. CHARM represents anything that may be the recipient of cultural value ascribed by any individual. CHARM not only represents the specific entities that might make up cultural heritage, but also other entities which are necessary in order to describe and understand them.

CHARM is a descriptive, rather than prescriptive, model. Users can pick and choose among the elements based on the needs of their organisation and project.

Some final words

What has your experience been with conceptual modelling? Have you had experience with ConML and has it been useful? Have you used other forms of conceptual modelling?

To find out more:

Reference:
Gonzalez-Perez, C. (2018). Information Modelling for Archaeology and Anthropology. Springer: Cham, Switzerland.

Biography: Cesar Gonzalez-Perez is a Staff Scientist at the Institute of Heritage Sciences (Incipit), Spanish National Research Council (CSIC), where he leads a co-research line in software engineering and cultural heritage. The ultimate goal of his work is to develop the necessary theories, methodologies and technologies to understand and assist the knowledge-creation processes that occur in relation to cultural heritage. Previously, Cesar has worked at a number of public and private organisations in Spain and Australia, both in industry and academia, and in the fields of conceptual modelling, metamodelling and situational method engineering. He has started three technology-based companies, served as elected member of the steering committee of the Computer Applications and Quantitative Methods in Archaeology (CAA) association, and authored or co-authored over 100 publications. Cesar’s current major areas of interest are the application of knowledge- and information-modelling techniques in the humanities, and the connection between inference, discourse and ontology evolution.

Modelado conceptual de temas complejos: ConML como ejemplo / Conceptual modelling of complex topics: ConML as an example

An English version of this post is available

¿Qué es un modelo conceptual? ¿Cómo pueden los modelos conceptuales representar temas complejos de forma efectiva y ayudar a la comunicación entre personas de diferentes disciplinas y formación?

Este artículo describe ConML, abreviatura de “Conceptual Modelling Language” (“Lenguaje de Modelado Conceptual”). ConML es un lenguaje de modelado diseñado para que los investigadores no expertos en tecnologías de la información puedan crear y desarrollar sus propios modelos conceptuales. Se puede aplicar en las humanidades, ciencias sociales, y ciencias experimentales.

¿Qué es un modelo conceptual?

Un modelo conceptual es una representación formal o semiformal de un tema de investigación, y que utiliza conceptos en vez de elementos materiales. Los modelos conceptuales suelen ser visualizados en forma de diagramas y texto complementario, como se muestra en la figura más adelante.

Un lenguaje de modelado es un lenguaje artificial, diseñado para expresar modelos. Ya que los modelos suelen mostrarse en forma de diagramas para mayor comodidad, los lenguajes de modelado suelen incorporar una notación gráfica. Igual que los lenguajes naturales, los lenguajes de modelado poseen:

  • un léxico, es decir, un conjunto de “palabras” que existen en el lenguaje
  • una sintaxis, es decir, un conjunto de reglas que nos dicen cómo se pueden combinar dichas palabras para componer “oraciones” con sentido
  • una semántica, es decir, una descripción de qué relación existe entre cada “palabra” del léxico y las cosas que representa

Las “palabras” de los lenguajes de modelado no se transmiten mediante texto o sonido como en los lenguajes naturales, sino que, habitualmente, lo hacen mediante iconos y dibujos, para ayudar a la visualización formal de los conceptos abstractos. Las reglas sintácticas del lenguaje de modelado nos dicen cómo se pueden conectar dichos iconos para expresar modelos, y qué significa cada tipo de conexión.

ConML contiene muy pocas “palabas”, ya que su sintaxis es muy simple.

El modelado conceptual fue desarrollado dentro de la disciplina de la ingeniería de software, y ha sido aplicado a muchos campos más allá de la misma, como la organización de empresas, la genómica o la arqueología. ConML pretende introducir conocimiento y técnicas que habitualmente solo están disponibles para los ingenieros de software a los especialistas de otros campos, incluyendo las humanidades y ciencias sociales.

Diagrama ConML de ejemplo, que muestra el hecho de los objetos arqueológicos completos suelen estar fragmentados, así como los subtipos que existen de ellos dependiendo de su origen e intencionalidad. (Fuente: Gonzalez-Perez 2018)

¿Cómo pueden los modelos conceptuales representar temas complejos de forma efectiva y ayudar a la comunicación entre personas de diferentes disciplinas y formación?

Existen dos asuntos clave:

  1. Un modelo conceptual nos puede ayudar a comprender mejor la porción del mundo con la que estamos tratando, ya que elimina parte del detalle y complejidad que a menudo la hacen inmanejable.
  2. Un modelo conceptual constituye un lenguaje mediante el cual podemos comunicar afirmaciones sobre dicha porción del mundo, especialmente en esas situaciones que involucran personas de diferentes disciplinas y formación.

El objetivo de ConML es comunicar ideas complejas de forma simple y efectiva.

ConML reconoce que distintas personas y grupos poseen a menudo diferentes puntos de vista subjetivos, que el mundo cambia a lo largo del tiempo, y que nuestro conocimiento de este no siempre es seguro. De este modo, la información que se recoge en un modelo conceptual puede ser, en menor o mayor grado, subjetiva, temporal e incierta. ConML incorpora mecanismos que facilitan la expresión de subjetividad y temporalidad y, hasta cierto punto, de vaguedad.

Al incorporar la capacidad de expresar subjetividad, temporalidad y vaguedad en los modelos – abordando de este modo los problemas habituales de multivocalidad, diacronía e imprecisión o incertidumbre – ConML permite expresar cosas que otros lenguajes no permiten, resultando así especialmente apropiado para las humanidades y ciencias sociales.

Ejemplo

Una de las mayores aplicaciones de ConML ha sido el desarrollo de CHARM, el Modelo de Referencia Abstracto del Patrimonio Cultural.

CHARM representa cosas en términos de conceptos, propiedades y relaciones. CHARM representa cualquier cosa que pueda ser receptora de valor cultural otorgado por cualquier individuo. CHARM no solo representa las entidades que de forma específica pueden componer el patrimonio, sino también otras cosas que son necesarias para describir y comprender las primeras.

CHARM es un modelo descriptivo, no prescriptivo. Los usuarios pueden escoger de entre los elementos que se ofrecen según las necesidades de su organización o proyecto.

Palabras finales

¿Qué experiencia ha tenido con el modelado conceptual? ¿Posee experiencia con ConML y, si es así, ha resultado útil? ¿Ha utilizado otras formas de modelado conceptual?

Para más información:

Reference:
Gonzalez-Perez, C. (2018). Modelado de Información para Arqueología y Antropología. Springer: Cham, Switzerland.

Biografía: César González-Pérez es Científico Titular en el Instituto de Ciencias del Patrimonio (Incipit) del Consejo Superior de Investigaciones Científicas (CSIC), donde lidera una línea de coinvestigación en ingeniería de software y patrimonio cultural. El objetivo último de su trabajo es desarrollar las teorías, metodologías y tecnologías necesarias para comprender y asistir los procesos de creación de conocimiento que ocurren en relación al patrimonio cultural. Previamente, César ha trabajado en diversas organizaciones públicas y privadas, en España y en Australia, tanto en la empresa como en la academia, y en los campos de modelado conceptual, metamodelado e ingeniería situacional de métodos. Ha fundado tres empresas tecnológicas, ha servido como miembro electo del comité directivo de la asociación Computer Applications and Quantitative Methods in Archaeology (CAA), y es autor o coautor de más de 100 publicaciones. Las áreas de mayor interés de César en la actualidad son la aplicación de técnicas de modelado de la información y el conocimiento en humanidades, y la conexión entre inferencia, discurso y evolución ontológica.

 

Scatterplots as an interdisciplinary communication tool

Community Member Methods , , , , , , ,

Community member post by Erin Walsh

erin-walsh
Erin Walsh (biography)

Scatterplots are used in many disciplines, which makes them useful for communicating across disciplines. They are also common in newspapers, online media and elsewhere as a tool to communicate research results to stakeholders, ranging from policy makers to the general public. What makes a good scatterplot? Why do scatterplots work? What do you need to watch out for in using scatterplots to communicate across disciplines and to stakeholders?

What makes a good scatterplot? Continue reading →

Developing a ‘capabilities approach’ for measuring social impact

Community Member Concepts , , , , , , ,

Community member post by Daniel J. Hicks

daniel-hicks
Daniel J. Hicks (biography)

Why do familiar metrics of impact often seem “thin” or to miss the point of research designed to address real-world problems? Is there a better way to measure the social impact of research?

In a recent paper (Hicks et al., 2018), my coauthors and I identified a key limitation with current metrics and started to look at how concepts from philosophy — specifically, ethics — can help us explain the goals of our research, and potentially lead to better metrics.

What’s the problem?

To understand the limitations of current metrics for measuring the social impact of research, it is useful to understand two distinctions, between resources and goals and between inward-facing and outward-facing goals for research. Continue reading →

Research impact in government – three crucial elements you will need for success

Community Member Processes , , , ,

Community member post by Anthony Boxshall

anthony-boxshall
Anthony Boxshall (biography)

What is the less visible ‘stuff’ that helps (or hinders) the uptake of research findings into government policy?

As a researcher it can be frustrating to have a great idea, connected to a seemingly important need, and even good networks, and yet still not be able to help your research have impact in the daily life of the relevant public sector decision-makers.

From more than 20 years of being involved in and with the senior decision-making levels of public sector environment agencies and running a business all about increasing the impact of science into public sector decision-making, I offer three insights that you should look for to see if the time and place are right for the uptake of your research. If these three elements exist, your research stands a good chance for uptake. Continue reading →

Transforming transdisciplinarity: Interweaving the philosophical with the pragmatic to move beyond either/or thinking

Community Member Theory , , , , , ,

Community member post by Katie Ross and Cynthia Mitchell

Katie Ross (biography)

Can a dive into the philosophical depths of transdisciplinarity provide an orientation to the fundamental purpose and need for transdisciplinarity?

The earlier philosophers of transdisciplinarity – such as Erich Jantsch (1980), Basarab Nicolescu (2002), and Edgar Morin (2008) – all aim to stretch or transcend the dominant Western paradigm, which arises in part from Aristotle’s rules of good thought. Aristotle’s rules of good thought, or his epistemology, state essentially that to make meaning in the world, we must see in terms of difference; we must make sense in terms of black and white, or dualistic and reductive thinking. Continue reading →