About

Joan Raeburn Holland is an Associate Professor of Harp at the University of Michigan School of Music, Theatre & Dance (SMTD). She is also the resident harpist and instructor of harp at the Interlochen Center for the Arts. Professor Holland maintains an active career in solo, chamber, concerto performances, and orchestral performing. She serves as the principal harpist for the Midland (MI) Symphony Orchestra, co-principal of the Traverse Symphony Orchestra in Traverse City, MI, and substitutes for various Michigan orchestras. Her previous roles include principal harp for the Phoenix Symphony Orchestra and the Cleveland Ballet Orchestra, and she has performed as a substitute harpist for the Cleveland Orchestra, the Pittsburgh Symphony, the Baltimore Symphony, and the Ohio Chamber Orchestra. An avid chamber musician, she frequently performs with colleagues at various venues and festivals, including the SMTD, Interlochen Chamber Music Concerts, the Lexington Bach Festival, and others. She has participated in recital programs for the American Harp Society and performed works such as the Sonata for Viola and Harp by Arnold Bax at the World Harp Congress and the International Viola Congress. Her educational background includes studies under Eileen Malone at the Eastman School of Music and Alice Chalifoux at the Cleveland Institute of Music, where she earned her Bachelor's degree in Harp Performance. She has also contributed to the harp community through her service as a Board member of the American Harp Society, helping to create educational resources for harp theory.

Research topics

• Computer Science
• Political Science
• Genetics
• Physics
• Virology
• Biology
• Fishery
• Knowledge management
• Immunology
• Public relations

Selected publications

• Practicing Inclusivity in AI: Stakeholder Engagement Policy in Action

  2024 · 1 citations

  • Computer Science
  • Political Science
  • Computer Science

  Despite growing demand for participatory approaches for AI development, there are challenges of ensuring ethical and inclusive stakeholder engagement and preventing "participant-washing" and perpetuating harm to marginalized communities. To help support AI-developing teams and practitioners more ethically and responsibly work with communities and the public, the Partnership on AI's Global Task Force for Inclusive AI proposed specific guidance and resources. This one-day workshop brings together researchers and practitioners to co-create context-specific stakeholder engagement strategies using these draft guidelines. The workshop will provide a platform to acquire new understandings of stakeholder engagement practices and exchange ideas and experiences with implementing stakeholder engagements. Through hands-on application and feedback, the workshop aims to develop participants' practical expertise in ethical stakeholder engagement and refine the guidelines to ensure its applicability in real-world contexts. We aspire to build an active community that supports inclusive AI-driven solutions based on more equitable relationships between developers and the communities who are impacted by the technologies created.

  DOI

• Characterization of antiviral immune responses in carp cell lines to inform on the development of fastidious carp virus infection modesl

  Developmental & Comparative Immunology · 2023

  • Biology
  • Virology
  • Immunology
  PublisherDOI

• Chapter 5 Social Practice, Strategy and Accountability

  University of Ottawa Press eBooks · 2022-12-31

  book-chapter1st authorCorresponding
  PublisherDOI

• Can There Be a Unified Theory of Complex Adaptive Systems?

  2018-03-08 · 18 citations

  book-chapter1st authorCorresponding

  Many of our most troubling long-range problems—trade balances, sustain-ability, AIDS, genetic defects, mental health, computer viruses—center on certain systems of extraordinary complexity. Despite appearances, the systems have enough significant characteristics in common to make it possible, even probable, that common general principles explain their dynamics. For this reason, the chapter aims to group these systems under a single classification at the Santa Fe Institute, calling them complex adaptive systems (CAS). The combination of internal models with a diversity of agents, along with the attendant nonlinearities, undercuts most traditional approaches to system dynamics. It is much easier to produce a definition of adaptive agent than it is to produce a general formal definition of CAS. Some experiments comparing the evolution of adaptive agents with tags to those without have been carried out. Even a cursory look uncovers many examples of natural agents wherein tags encourage diversity and complexity.

  PublisherDOI

• The Global Economy as an Adaptive Process

  2018-11-15 · 31 citations

  book-chapter1st authorCorresponding

  The global economy is an example, par excellence, of an adaptive nonlinear network (ANN). Other ANNs are the central nervous system, ecologies, immune systems, the developmental stages of multi-celled organisms, and the processes of evolutionary genetics. In the global economy, the anticipation of an oil shortage or of a significant default of foreign loans can have profound effects upon the course of the economy, whether or not the anticipated events come to pass. A direct way to apply classifier systems to the study of the global economy is to find an established model from economics that, even in its simplest form, raises some of the central quandries of the global economy. In an economy, as in ANNs in general, accumulated experience provides increasingly refined standard operating procedures and progressively more sophisticated interactions between them. In mathematical terms, operation is far from any global attractor and strategies are faced with perpetual novelty.

  PublisherDOI

• Social and economic impacts of subsidy policies on rural development in the Poyang Lake Region, China: Insights from an agent-based model

  Agricultural Systems · 2016-06-25 · 38 citations

  article
  PublisherDOI

• Cycles and Hierarchies

  Exploring complexity · 2016-09-26 · 1 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Complexity: A Very Short Introduction

  2014-07-24 · 517 citations

  book1st authorCorresponding

  Abstract From the movement of flocks of birds to the Internet, environmental sustainability, and market regulation, the study and understanding of complex non-linear systems has become highly influential over the last thirty years. Complexity: A Very Short Introduction introduces the key elements and conceptual framework of complexity. From complex physical systems such as fluid flow and the difficulties of predicting weather, to complex adaptive systems such as the highly diverse and interdependent ecosystems of rainforests, it combines simple, well-known examples—Adam Smith’s pin factory, Charles Darwin’s comet orchid, and Herbert Simon’s “watchmaker”—with an account of the approaches, involving agents and urn models, taken by complexity theory.

  PublisherDOI

• Complex systems

  2014-07-24 · 1 citations

  book-chapter1st authorCorresponding

  Abstract What is complexity? A complex system, such as a tropical rainforest, is a tangled web of interactions and exhibits a distinctive property called ‘emergence’, roughly described by ‘the action of the whole is more than the sum of the actions of the parts’. This chapter explains that the interactions of interest are non-linear and thus hierarchical organization is closely tied to emergence. Complex systems explains several kinds of telltale behaviour: emergent behaviour, self-organization, chaotic behaviour, ‘fat-tailed behaviour’, and adaptive interaction. The field of complexity studies has split into two subfields that examine two different kinds of emergence: complex physical systems and complex adaptive systems.

  PublisherDOI

• Emergence

  2014-07-24

  book-chapter1st authorCorresponding

  Abstract ‘Emergence’ looks at the relations between building blocks, generated systems, and the phenomenon of emergence. To understand emergent phenomena, it is necessary to describe the emergence of a system’s behaviour from the non-additive interactions of its building blocks. Emergence occurs when the generators for a generated system combine to yield objects having properties not obtained by summing properties of the individual generators. Co-evolution, often mediated by tags, is one of the major mechanisms for generating non-linear interactions between CAS agents. Tags serve as building blocks but can also be constructed from other building blocks. Tag recombination provides a general mechanism for emergence, because signal-processing lies at the heart of all complex systems.

  PublisherDOI

Frequent coauthors

• Blake LeBaron

  8 shared

• W. Brian Arthur

  8 shared

• R. G. Palmer

  6 shared

• Rick Riolo

  University of Michigan–Ann Arbor

  5 shared

• Richard E. Nisbett

  5 shared

• Lashon B. Booker

  Mitre (United States)

  5 shared

• Paul Thagard

  5 shared

• Jinyun Ke

  5 shared

Labs

• StringsPI