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A Systems Approach to Establishing Scientific Integrity in Evidence Based Policy Making


A Systems Approach to Establishing Scientific Integrity in Evidence Based Policy Making Prof. Dr. Wijnand J. Swart Centre for Plant Health Management (CePHMa) – PowerPoint PPT presentation

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Title: A Systems Approach to Establishing Scientific Integrity in Evidence Based Policy Making

A Systems Approach to Establishing Scientific
Integrity in Evidence Based Policy Making
  • Prof. Dr. Wijnand J. Swart
  • Centre for Plant Health Management (CePHMa)
  • Faculty of Natural and Agricultural Sciences
  • University of the Free State
  • Bloemfontein
  • South Africa
  • swartwj.sci_at_ufs.ac.za

EVIDENCE in Policy Context
  • Sound, credible and robust evidence, whether
  • quantitative
  • qualitative
  • statistical
  • economic
  • attitudinal / behavioural
  • anecdotal
  • social
  • opinion based
  • or review based .....
  • ......is an essential and necessary part of
    the enabling environment for formulating policies
    that are coherent and effective in terms of their

The Policy Making Process
  • Techniques, analyses and judgements used to
    evaluate and formulate data and information into
    knowledge / evidence for making effective
    policies are critical.

What is Sound Evidence?
  • Concept of sound and credible evidence is very
  • Dependant on inter alia
  • types
  • sources
  • generating techniques
  • context
  • understanding
  • ...... of data / information / knowledge

Sound Evidence Context Understanding
  • Data are facts (e.g. numbers, names, symbols) and
    have little value in themselves.
  • Information relates to description, definition,
    or perspective (what, who, when, where).
  • Knowledge comprises strategy, practice, method,
    or approach (how).
  • Wisdom embodies principle, insight, morals, or
    archetypes (why).
  • Absolute Truth ?

understanding principles
understanding patterns
understanding relations
Knowledge vs Science
  • Science is organized knowledge. Herbert Spencer
  • Science a knowledge of principles and
    causes. (Webster's Revised Unabridged
  • Science is a way of thinking much more than it is
    a body of facts. Carl Sagan
  • Good Science could be defined as those
    practices which contribute most to advances in
  • Sindermann The Joy of Science 1985.
  • Science is to see what everyone else has seen but
    think what no one else has thought. Albert

Science and Policy Making
  • Creative and innovative contributions of
    scientists and policy makers and the trust
    engendered in them by the public, to whom they
    are accountable is of paramount importance.
  • Follows therefore that fostering an environment
    that promotes research or scientific integrity is
    an integral part of that accountability and the
    pursuit of new knowledge.

Scientific Integrity
  • Integrity by definition
  • honesty
  • a state of being entire or whole
  • Perspectives of scientific integrity
  • Ethical issues relating to misconduct (fraud)
    or manipulation, suppression, or distortion of
  • 2. Striving towards wholeness or excellence
    in the search for knowledge.

Research Integrity
  • Essential for maintaining scientific excellence
    and for keeping the publics trust.
  • Research integrity characterizes
  • Institutional integrity
  • creating an environment that promotes responsible
    conduct and high levels of integrity
  • embracing standards of excellence,
    trustworthiness, and lawfulness
  • Individual integrity Scientists commitment to
    intellectual honesty and personal responsibility
    and is an aspect of moral character and
    experience. A good scientist must
  • communicate well
  • obtain research grants
  • excel in teaching and mentoring
  • engage in ethical decision making
  • use knowledge wisely to plan and execute

Institutional Integrity Politics vs Science
  • Politicization of science as old as science
    itself e.g Galileo's theory that the Earth
    revolves around the sun perceived as a challenge
    to the authority of the Catholic church.
  • Political interference threatens the integrity of
    government science and policy making all over the
  • Manipulation, suppression, and distortion of
    government science misinforms public and leads to
    poor policy decisions.
  • Especially rife in developing countries, e.g. the
    assertion of the South African president Thabo
    Mbeki that AIDS is not caused by HIV flew in the
    face of decades of research and threatened to
    undermine proper treatment of the disease.

Bush Administration's Misuse of Science
  • On February 18, 2004, 62 pre-eminent scientists
    AND researchers released a statement titled
    Restoring Scientific Integrity in Policy Making
    in the USA.
  • Scientists charged the Bush administration with
    widespread and unprecedented manipulation of the
    process through which science enters into its
  • Scientists accused Bush administration of
  • Epidemic altering and concealing of scientific
    information by senior officials in various
    federal agencies.
  • Active censorship of scientific information that
    the administration considered threatening to its
    own philosophies
  • Restriction of the ability of government-supported
    scientists to freely communicate scientific
    ideas related to "sensitive" issues .

Integrity of Research Institutions
  • The organizational structure and processes that
    typify the mission and activities of a research
    institution can either promote or detract from
    the responsible conduct of research.
  • These process are in part determined by the
    external environment and are influenced by the
    dynamics between and among organizational
  • Any element or part of an organization can be
    viewed as a system in and of itself.
  • External conditions influence the inputs into an
    organization, affect the reception of outputs
    from an organizations activities, and directly
    affect an organizations internal operations.

  • Open systems model of internal environmental
    elements of a research organization showing
  • Inputs / resources for organizational functions
  • Structures and processes that define an
    organizations operation
  • Outputs / outcomes of activities carried out by
    individual scientists, research groups or teams,
    and other research-related programs.

Source National Academy of Sciences -
  • Interrelatedness between research organizations
    and the various external influences that have an
    impact on integrity in research.
  • Systems and subsystems of the external-task
    environment are embedded within the general
    sociocultural, political, and economic
  • Relationships also exist between and among the
    elements within the external environment.

Source National Academy of Sciences -
Holistic Knowledge / Evidence
  • Rather than focus on the ethical or moral aspects
    of scientific integrity, focus here is on the
    process of generating data and information and
    integrating it into sound knowledge (sound
    evidence) for decision-making.
  • Integrity by definition
  • honesty
  • a state of being entire or whole
  • Integrate by definition
  • to combine parts into a whole
  • A whole.. is more than the sum of its parts.

Jan Christian Smuts, Holism and Evolution 1926
Sound Evidence A Holistic View
  • A collection of data is not information.
  • A collection of information is not knowledge.
  • A collection of knowledge is not wisdom.
  • Information, knowledge, and wisdom are more than
    simply collections
  • Each concept represents more than the sum of its
    parts and has a synergy of its own.

  • A system is defined as a set of interacting
    units with relationships among them. The
    properties (or behaviour) of the system as a
    whole emerge out of the interaction of the
    components comprising the system.
  • The interactions of the parts become more
    relevant to understanding the system than
    understanding the parts.
  • This definition of a system implies something
    beyond cause and effect.

The Ultimate System
  • In truth only one system, the Universe"
  • All systems are sub-systems of a larger system.

solar system
Systems Thinking and Policy
  • Science is a way of thinking much more than it is
    a body of facts. Carl Sagan
  • Systems thinking offers a conceptual framework
    or model for thinking differently.
  • Systems thinking has permeated many scientific
    fields including education, business management,
    human development, sociology, psychology,
    agriculture, ecology and biology, earth sciences.

Hard vs Soft Systems
  • Adopting a systemic perspective on solving policy
    problems therefore appear to offer a useful way
    of correcting these deficiencies.
  • In 1960s, a hard (quantitative) systems
    approach was touted as the policy science.
  • However, hopes not realized for variety of
    reasons its comprehensive modelling too
    information-intensive and mathematical.
  • The soft (qualitative) systems approach of
    systems thinking has increasingly been used since
    the 1990s as a paradigm in policy planning and
  • Soft systems methods stress the self-organizing
    and adaptive capacities of appropriately designed

Hard vs Soft Systems
  • Soft systems methods
  • Subjective (interpretive) philosophy
  • Systems sociological theory base
  • Flexible methodology
  • Organizational problem-solving focus
  • Creative / intuitive
  • Analyst is facilitator
  • Participative
  • Organizational learning outcomes
  • Several ambiguous outcomes
  • Hard systems methods
  • Objective philosophy
  • Computer science systems theory
  • Rigid method
  • Data, process, database technical focus
  • Scientifically analytical
  • Analyst is expert
  • Analyst dominated
  • Computer design outcomes
  • One correct solution

A hard systems view of a farming system a
biological network suitable for mathematical
A soft systems view of a farming system an arena
for gaining experience and increased
Source Robinson, B. 2003. 11th Australian
Agronomy Conference)
Soft Systems Methodology (SSM)
  • Can be used both for general problem solving and
    in the management of change.
  • Used in the analysis of complex situations where
    there are divergent views about the definition of
    the problem "soft problems" or policy options
    (e.g. How to improve health services delivery
    How to manage disaster planning).
  • At the heart of SSM is a comparison between the
    world as it is, and some models of the world as
    it might be. 
  • Out of this comparison comes a better
    understanding of the world ("research"), and some
    ideas for improvement ("action").

SSM for Problem Solving
  • Classic form of SSM consists of seven steps
  • Problem unstructured by researchers
  • Problem situation expressed to capture rich
  • Create root definitions of relevant systems (i.e.
    social, political environmental)
  • Making and testing conceptual models based upon
    world views
  • Comparing conceptual models with reality
  • Identifying feasible and desirable changes
  • Acting to improve the problem situation

understanding and improvement
conceptual models
  • Differences between models and reality become the
    basis for planning and policy making process.

Multi-agent systems (MAS)
  • Policy increasingly has to address topics that
    have to do with disequilibrium, dynamics, and
  • The overwhelming complexity of biophysical and
    socio-economic constraints that increasingly
    characterize rural areas in developing countries
    necessitates the development of more
    sophisticated tools to support policy making in
    these areas.
  • Multi-agent systems (MAS) are a relatively new
    field in computer science that have been proposed
    as a modelling approach for establishing even
    higher levels of scientific integrity in the
    generation and evaluation of evidence for making
  • Analogous to artificial intelligence.

MAS for Policy Making
  • Multi-agent models might be the preferred choice
    when heterogeneity and interactions of agents and
    environments are significant and policy responses
    cannot be aggregated linearly.
  • MAS can thus complement bio-economic simulation
    models which cannot fully capture the
    heterogeneity in biophysical and socio-economic
    constraints and the interactions between them.
  • There are several policy questions in the context
    of agricultural development of rural areas where
    MAS simulations may generate useful information
    for decision making on public investments in RD
    and the targeting of policy interventions.
  • Examples of such policy questions
  • Should funds be spent on crop breeding for stress
    resistance or in research for improved crop
  • Should micro-finance be promoted or should
    agricultural inputs be subsidized?

  • An agent is anything that can be viewed as
    perceiving its environment through sensors and
    acting upon that environment through effectors
  • Agents may be persons, farms, markets, computer
    programmes or anything that is reactive,
    autonomous, and goal-oriented.
  • Agents may have the ability to communicate with
    other agents, learning, mobility, and
    flexibility. May even have personality and show

Agent Flexibility
  • An intelligent agent is capable of flexible
    autonomous action.
  • Reactive A reactive system is one that maintains
    an ongoing interaction with its environment, and
    responds to changes that occur in it (in time for
    the response to be useful)
  • Pro-active Generating and attempting to achieve
    goals not driven solely by events taking the
    initiative i.e. goal directed behavior
    recognizing opportunities
  • Social Ability to interact with other agents via
    some kind of agent-communication language, and
    perhaps even cooperate with others.

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  1. Balmann, A. 2000. Modeling land use with
    multi-agent systems perspectives for the
    analysis of agricultural policies.
  2. Berger, T. and Ringler, C. 2002. Trade-offs,
    efficiency gains and technical change Modeling
    water management and land use within a
    multiple-agent framework, Quarterly Journal of
    International Agriculture 41119144.
  3. Berger, T., Schreinemachers, p., and Woelcke, J.,
    2006. Multi-agent simulation for the targeting
    of development policies in less-favored areas.
    Agricultural Syatems 8828-43.
  4. Checkland, P.  1981 Systems thinking, systems
    practice.  Chichester Wiley. 
  5. Checkland, P., and Holwell, S. 1998  Information,
    systems, and information systems making sense of
    the field.  Chichester, UK Wiley.  
  6. Checkland, P.  and Scholes, J.  1991 Soft systems
    methodology in action.  Chichester Wiley. 
  7. Harrison MI. 1994. Diagnosing Organizations
    Methods, Models, and Processes, 2nd ed. Thousand
    Oaks, CA Sage.
  8. Schreinemachers, P. Berger, T. and Aune, J.B.,
    2007. Simulating soil fertility and poverty
    dynamics in Uganda A bio-economic multi-agent
    systems approach. Ecological Economics
  9. Union of Concerned Scientists. 2004. Scientific
    integrity in policy making An investigation into
    the Bush administration's misuse of science.
    Cambridge (Massachusetts) Union of Concerned
    Scientists 49 pp.
  10. Woolridge, M. 2002. An Introduction to Multiagent
    Systems by John Wiley Sons (Chichester,

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