Participatory Information Systems Appraisal (PISA) represents a shift in the predominant way of thinking about information for economic and social development. Developed in Mongolia over a four-year period by Pact, PISA adapts a well developed family of Participatory Rural Appraisal (PRA) tools for today’s information-intensive economy, where new Information and Communication Technologies (ICT) are increasingly promoted as tools for poverty alleviation and sustainable human development.

The PISA approach systematically introduces and explains the concepts and strategies needed to make well informed,, data-based decisions while empowering key stakeholders in the process. Recognizing the need for rapid information exchange in an information-intensive world, the PISA process establishes a lasting information channel in the heart of the information channel in the heart of the information channel very community a program seeks to support.

This study explores how communities and companies can engage in co-planning and monitoring to ensure sustainable local development benefits from the extraction of resources. Key points are highlighted in six sections, which include: (1) the extractive industries context; (2) the business case and the community case for engagement using participatory planning and monitoring tools; (3) participatory tools within the different stages of extractive project development; (4) potential areas for co-planning and monitoring; (5) key challenges; and (6) conclusions.

A sample of participatory monitoring and evaluation tools and mechanisms include:

  • Participatory planning: Members of local communities contribute to plans for company activities potentially relating to business and to local development.
  • Good Neighbor Agreements are co-produced commitments constructed and agreed between companies and communities.
  • Community Forums: Single or multi-stakeholder community groups gathering voluntarily for discussion on a previously agreed upon topic, to provide information and receive feedback, or for other relationship-building activities that are made explicit. Effective communication strategies are required to ensure balanced participation.
  • Community Suggestion Boxes: Suggestion box placed in an easily accessible public location. Members of a community may submit anonymous complaints, suggestions or questions. Box is opened publicly at pre-determined times (such as weekly) and a response is provided to each suggestion.
  • Participatory Budgeting: Processes by which citizen-delegates decide on or contribute to decisions regarding the allocation and monitoring of expenditures of all or a portion of public resources. Also applicable to company resources allocated for community development.
  • Citizen Report Cards: Short surveys with questions developed through participatory discussion and used to measure perceptions of adequacy and quality of public services. They are also potentially applicable to the extractive industry context. Survey responses are supplemented with a qualitative understanding.
  • Community Scorecards: Focus groups identify indicators of success for a given project or service. Target beneficiaries and service providers rate the effectiveness of service based on the agreed upon indicators.

Underlying each of these tools are supporting processes of training and capacity building, access to information, and mutually agreed-upon metrics for monitoring.


SIENA is a program for the statistical analysis of network data, with the focus on social networks.
Networks here are understood as entire (complete) networks, not as personal (egocentered) networks: it is assumed that a set of nodes (social actors) is given, and all ties (links) between these nodes are known – except perhaps for a moderate amount of missing data.
SIENA is designed for analyzing various types of data as dependent variables:

Longitudinal network data:
This refers to repeated measures of networks on a given node set (although it is allowed that there are some changes in the node set). Models can be specified with actor-oriented as well as tie-oriented dynamics.

Longitudinal data of networks and behavior:

This is like longitudinal network data, but in addition there are one or more changing nodal variables that are also treated as dependent variables, and referred to as behavior. The network will influence the dynamics of the behavior, and the behavior will influence the dynamics of the network. In other words, this is about the co-evolution of networks and behavior.

Cross-sectional network data.

‘Cross-sectional’ means that only one observation is available. This method uses exponential random graph models (‘ERGMs’), also called p* models.
The ERG model is implemented in SIENA version 3, but not any more in version 4 (RSiena).

The name SIENA stands for Simulation Investigation for Empirical Network Analysis.

The main approach used by SIENA for modeling dynamics of network (or of networks and behavior) is an actor-oriented model, in which it is assumed that the social actors who are represented by the nodes in the network play a crucial role in changing their ties to other actors; in the case of associated behavior dynamics, also in changing their behavior. All of these models are Markov chain models; such models are more applicable to relations and behavioral variables that can be regarded as states than to relations or behavior that are more adequately regarded as non-enduring events.

The statistical analysis in SIENA is done on the basis of computer simulation of the network. This can be time consuming. In view of the detailed approach to network dynamics and the required computing resources, the method is applicable in principle to networks on approximately 10 to 1,000 nodes.

A scientific summary is given below. The methods implemented in SIENA are described in the papers given in the webpage with literature. Some further articles with applications are given in the webpage with further applications. The program is obtained as a package within Rand the extensive manual is downloadable here.

There exists a User Group for Siena and StOCNET to exchange information and seek technical advice on using the Siena and StOCNET software to analyze network data.
The address is http://groups.yahoo.com/groups/stocnet/.

SIENA version 4 is also called RSiena, a contributed package for the R statistical system, which can be downloaded from http://cran.r-project.org.
The incorporation of RSiena in R makes available all other possibilities offered by R; in particular, to execute R in a Mac or Unix/Linux environment. Further information on RSiena is on the RSiena page.

The transition from the Windows-based SIENA 3 to the R-based, multi-platform SIENA 4, also called RSiena, is complete since Summer 2011, in the sense that practically all longitudinal functionalities of SIENA 3 have been implemented for SIENA 4; since then, many new functionalities have been added, and this is still going on.

The SIENA 3 program also contains methods for analyzing Exponential Random Graph Models (ERGMs). For the latter methods, users now are referred to the standalone program pnet or the R package statnet, although the old SIENA version 3 still is available for those who wish to use it.
Further work on the StOCNET interface is discontinued.

Known bugs and new papers are given at the news webpage.

The SIENA program is part of an ongoing research effort. The research team is composed of Tom SnijdersChristian SteglichJohan KoskinenJosh Lospinoso, Charlotte Greenan, Nynke Niezink, and Christoph Stadtfeld, with earlier contributions from Ruth Ripley, Krists Boitmanis, Paulina Preciado, Michael SchweinbergerMark Huisman, and Marijtje van Duijn. Current research projects are given at theresearch projects webpage. Courses and a user group are mentioned at the activities webpage.

SIENA version 3 was written in Delphi, for use under Windows. For those who still wish to use Siena 3 it is strongly advised to use the most recent version. This can be downloaded from the downloads page.

The SIENA 3 program can be executed in two ways. Many people find it convenient to execute it from the StOCNET environment. It is also possible to execute SIENA 3 as a stand alone program. The StOCNET project was an activity of Christian Steglich, Tom Snijders, andSciencePlus (Minne Oostra), with important earlier contributions from Evelien Zeggelink, Peter Boer, Bert Straatman, Mark Huisman, and others.

PRIMA helps executives and public officials manage high-impact risks and opportunities in socio-political systems in order to identify and influence stakeholder coalitions to influence public policy and corporate reputation.


Mission Statement

PRIMA identifies and quantifies high-impact risks and high-yield opportunities in complex sociopolitical and infrastructure systems. We provide our clients with actionable insights by analyzing vast quantities of data to identify the stakeholder positions with the greatest potential impact on financial performance or mission attainment.

Markets We Serve

PRIMA’s clients share a common concern: the profound impact that critical stakeholder coalitions or complex system interactions may have on financial performance or mission attainment. Whether the system in question is…

  1. Bullet a political system in which policymakers with different powers seek to accommodate organized interest groups with different priorities, or
  2. Bullet a social system in which organized interest groups attempt to shape the perceptions of ordinary consumers and voters

PRIMA assists private executives and public officials by identifying the stakeholder coalitions or infrastructure system architectures toward which limited risk mitigation resources can be most effectively directed.

For more information, see presentation here

NB: Witold J. Henisz is a principal in PRIMA LLC

ListenLogic is a pioneer of advanced social insight discovery, utilizing next-generation streaming big data technology to deliver actionable, real-time intelligence to the world’s largest enterprises.

ListenLogic Advanced Social Analytics delivers deep insight into consumer attitudes and needs by analyzing unstructured data from social media, open-end surveys and text documents. Our Risk & Reputation Monitoring detects reputational and operational risks that emerge from social media and internal data. Our Pharma & Health division, ListenLogic Health,specializes in providing brand and market intelligence to life science companies.

To see how ListenLogic can help your business contact us.