Modeling Autonomous Decision-Making onEnergy and Environmental Management UsingPetri-Net: The Case Study of a Community inBandung, Indonesia
Niken Prilandita *, Benjamin McLellan and Tetsuo Tezuka
Graduate School of Energy Science, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan;email@example.com (B.M.); firstname.lastname@example.org (T.T.)* Correspondence: email@example.com; Tel.: +81-75-753-4739; Fax: +81-75-753-9189
Academic Editor: Palmiro PoltronieriReceived: 28 December 2015; Accepted: 5 April 2016; Published: 14 April 2016
Abstract: Autonomous decision-making in this study is defined as the process where decision-makershave the freedom and ability to find problems, select goals, and make decisions for achieving theselected problems/goals by themselves. Autonomous behavior is considered significant for achievingdecision implementation, especially in the context of energy and environmental management, wheremultiple stakeholders are involved and each stakeholder holds valuable local information for makingdecisions. This paper aims to build a structured process in modeling the autonomous decision-making.A practical decision-making process in waste-to-energy conversion activities in a community inBandung, Indonesia, is selected as a case study. The decision-making process here is considered asa discrete event system, which is then represented as a Petri-net model. First, the decision-makingprocess in the case study is decomposed into discrete events or decision-making stages, and thestakeholders’ properties in each stage are extracted from the case study. Second, several stakeholderproperties that indicate autonomous behavior are identified as autonomous properties. Third,presented is a method to develop the decision-making process as a Petri-net model. The model isutilized for identifying the critical points for verifying the performance of the derived Petri-net.
Keywords: autonomy; decision-making; Petri-net; energy; environmental; community; Indonesia
The recent global agenda and technological challenges for creating a more sustainableenvironment have encouraged countries around the world to gradually shift towards sustainableenergy transitions. Upon the new global agreement of Sustainable Development Goals, every countryis now highly anticipated to direct their efforts towards realizing a more sustainable energy system andenvironment . From the technology side, the emergence of new technologies, such as smart gridsand source-centered renewable energies, have expanded the potential and requirements of energygeneration and management in ways that have not been available previously. These facts suggest thatthe energy system is likely to become more distributed and localized, thus the decision-making andpolicy-making process in the energy sector should be adjusted to follow this future tendency .
Most decisions made on energy and environmental management affect a large number of peopleand, thus, are of public interest. Decision-making in this sector usually becomes complicated sincevarious interests need to be accommodated in the process. Moreover, once a consensus has beensuccessfully reached, it does not guarantee successful implementation. Various decision-makingapproaches for reaching an easy consensus, as well as for achieving successful implementation,have been proposed. Two common approaches in decision-making are with the centralized and the
Challenges 2016, 7, 9; doi:10.3390/challe7010009 www.mdpi.com/journal/challenges
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decentralized approaches [3,4]. The quest of balancing between the centralized and the decentralizedsystems for decision-making is often an issue in organizational management. Easy access toinformation with the advancement of information technology, the internet, and other means today,have made the decision-making style in organizations lean towards a more decentralized style [5,6].However, this approach may not be entirely applicable for cases in energy and environmentalmanagement that occur in the public domain.
This study puts more focus on autonomy in decision-making processes regardless of whether theyare conducted under a centralized or a decentralized system. Two ways of understanding the conceptof autonomy are considered here. Firstly, autonomy in the political or public administration field,which is often seen as one of the traits of a more decentralized system . Secondly, as understoodin the current study, autonomy can be considered as a property of persons regardless of the systemiccontext [7–9]. Therefore, we argue that autonomy can exist in both centralized and decentralizedapproaches because autonomy is the property of each decision-maker.
The hypothesis of this study is that decisions made autonomously are more likely to achievesuccessful outcomes. Autonomy in making decisions is believed to be related to an increase in qualityof life. Research from neuroscience has found that actively making decisions can boost pleasure andincrease the decision-makers’ happiness, satisfaction, and perceived control . Furthermore, highlevels of happiness and satisfaction are causal influences on success and achievement, not the otherway around . Simply stated, if a decision-maker has made an autonomous decision, without beingcoerced or forced, it is considered more likely that the decision-maker will achieve the decision goaland benefit from that.
Normatively, stakeholders’ autonomy in making decisions is important, though its importantrole in decision-making may not been objectively examined . The fact that we have not foundstudies that objectively examined the role of autonomy in decision-making in energy-environmentalmanagement showed that this theme has to date been insufficiently examined. We argue that therecent global agenda and technological advances in the energy-environmental sector (e.g., smart-gridtechnologies, decentralized energy, and market liberalization) expect decision-makers to become moreautonomous. This situation has created the necessity to develop a framework that can represent andidentify the role of stakeholders’ autonomy in the decision-making process. Such a framework wouldconsist of several elements employed for specific tasks, and is the purpose of the current research.This paper discusses one of the important elements of the framework, a model that aims to represent,analyze, and simulate the autonomous decision-making process.
The autonomous decision-making model in this paper is developed as a discrete event system,and this paper presents the method to build such a model. The decision-making process isdecomposed into discrete events that we call decision-making stages. Afterwards, the propertiesof stakeholders involved in each stage are identified; thus, the concept of a discrete event systemfor autonomous decision-making is established. Petri-net is utilized to represent the discrete eventsystem of the autonomous decision-making process. Each decision-making stage, the stakeholders’properties, and the state after decisions are made; corresponding to a small Petri-net modelconsisting of a few transitions and places. The autonomous decision-making model is constructedby combining all of these small Petri-net models of each event/stage. As an addition, we conductedanalysis of the Petri-net model’s behavior for identifying the stages which are indispensable for anautonomous decision-making system. These stages are called the critical points in the autonomousdecision-making process.
2. The Definition of Autonomous Decision-Making
This section explains the definition of autonomous decision-making. The term, autonomousdecision-making is defined by dissecting it into the root words comprising it, which are “autonomy”and “decision-making”. The development of the concept of autonomy as a political and personalproperty is historically explained, followed by a brief explanation on various scopes of the
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decision-making process, and various types of energy decision-making. Based on this information, weconstruct the definition of autonomous decision-making used in this study.
2.1. The Concept of Autonomy
The definition of autonomy has been through several changes throughout the course of history.As mentioned above, there are at least two different concepts of autonomy explained in this paper.Autonomy originated from the Greek words “auto” which means self, and “nomos” which means law.This concept was firstly coined referring to the city states in ancient Greece that were self-governing.Originally, autonomy was defined in a political manner, which was the right of the states (or city-states,in that instance) to administer their own affairs . In the context of public administration management,territorial or local autonomy is the result of a decentralization process . In the Indonesian contextfor example, the Law of Decentralization number 22/1999, was the beginning of the country’s journeytowards a more decentralized political structure. This law has since become the legal basis forproviding more autonomy to local governments in making decisions regarding their own territoryand environment. The spirit of the law has had a side effect, however, in that it caused the Indonesianpeople to gain greater awareness of autonomy, knowing that they had more freedom in choosingamong options. This has promoted decision-making processes to be performed more autonomouslyin various levels of society’s hierarchical structure, including at the lower authority levels, such asvillages and sub-districts . Looking at this fact, the term autonomy in Indonesia has graduallybecome understood not only as the property of a state or territory, but also as a personal trait.
One of the most important moments in the history of the concept of autonomy was when thedefinition of autonomy was transformed from the property of a state in the ancient Greek era, into aproperty of persons during the Renaissance era [7,8]. Since then, the concept of autonomy has beenunderstood in both ways. However, autonomy in the majority of contemporary works is seen as aproperty of persons, or personal autonomy . Although the concept of autonomy mainly revolvesaround these two definitions, the dimensions of autonomy are understood in many different ways,depending on which field of study is viewing it. Mackenzie, for example, defined three dimensionsof autonomy, namely self-determination, self-governance, and self-authorization . Other studiesfocus on the self-directedness and resoluteness dimensions of autonomy . Meanwhile, the computerscience and information technology fields view the ability to continuously learn or self-learning traitsin the emergence of autonomous machines or artificial intelligence as one of the most importantcharacteristics of autonomy .
2.2. Decision-Making Process
The definition of decision-making has been long established, and since decision-making isunderstood as a process of making decisions, then the definitions mostly evolved on the scopeof the process. There are two predominately different views in decision theory regarding the extentof the decision-making scope. Firstly, decision-making is defined as a process started by identifyingproblems or goals, and ended after a decision has been made. One of the main supporters of thisconcept was Herbert Simon (1960) . Later, Huber (1980) expanded the concept of decision-makingby defining it as “the process through which a course of action is taken” , and the process bywhich the decision is implemented is considered as part of the problem-solving process. Most of thestudies that defined the decision-making process came from the field of organizational management.Meanwhile when decisions need to be made in the public domain, the decision-making process is oftenregarded as the whole cycle from problem identification up to decision implementation and evaluation,and then feeding-back to problem identification. This is known as a generic decision cycle , or aplanning process . An example of a decision-making cycle is presented in Figure 1. In this study,we investigate the decision-making process extended to the implementation stages.
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Figure 1. Example of a decision-making cycle [18,19].
2.3. Energy-Environmental Decision-Making at Various Stakeholder Levels
The following section explains decision-making in energy and environment by various
stakeholders, such as national government, local government (provincial/city/regency governments,
and formal agencies/bodies within these local governments), community, household and individual
(households and individuals are considered as a single decision-maker), and non-governmental
institutions (i.e., international and local NGOs, business or private sector stakeholders, media,
experts and academicians). As mentioned earlier, decision-making in energy and environmental
management often becomes complex because it occurs in the public domain and, therefore, various
stakeholders are involved in it. According to Sexton, et al. , the main stakeholders that are usually
involved in environment-related decision-making are national governments, regional or local
government bodies, business associations, environmental advocacy groups, community or
neighborhood groups, and affected or interested individuals. The relationships between these
stakeholders can be classified into two types of relationship, which are vertical (hierarchical) and
horizontal (parallel) relationships with each other [21,22]. Decision-making for individual
stakeholders and groups of stakeholders is influenced both by the structure of relationships and the
characteristics of the individual stakeholders.
Energy related decision-making and policy-making (We use the phrase “energy (and
environmental) decision-making and policy-making” or “decision-making in energy sector”
interchangeably in this paper because the research object is related with both energy and
environmental sector.) at the national level tends to occur in a top-down manner, following the
hierarchical structure of the country’s institutions. In the UK, for example, energy decision-making
functions have historically been performed mainly by the central government and large corporations
in the private sector. This situation began to change after the Localism Bill was stipulated in 2010
aiming to shift decision-making power from central governments to individuals, communities, and
local government [23,24]. Another example is from a developing country, Indonesia, where for more
than two decades since the first national energy policy was introduced in 1981, the key strategic
energy decisions and policies are made centrally by the national government . The role of local
government in the energy sector was recognized after the promulgation of the Energy Act in 2007.
The act mandates each local government to formulate its own local energy masterplan, based on the
targets outlined by the national energy masterplan.
Recent experiences from both countries have shown that the local authorities are mandated and
expected to have more capacity in energy decision-making functions. The long period of centralized
energy decision-making experience in both countries has created a great challenge for the local
authorities to pick up the task. Lack of capacity of the local government with regards to energy
planning, and limited guidelines on how to formulate the masterplan itself, are some of the
Figure 1. Example of a decision-making cycle [18,19].
2.3. Energy-Environmental Decision-Making at Various Stakeholder Levels
The following section explains decision-making in energy and environment by variousstakeholders, such as national government, local government (provincial/city/regency governments,and formal agencies/bodies within these local governments), community, household and individual(households and individuals are considered as a single decision-maker), and non-governmentalinstitutions (i.e., international and local NGOs, business or private sector stakeholders, media, expertsand academicians). As mentioned earlier, decision-making in energy and environmental managementoften becomes complex because it occurs in the public domain and, therefore, various stakeholdersare involved in it. According to Sexton, et al. , the main stakeholders that are usually involved inenvironment-related decision-making are national governments, regional or local government bodies,business associations, environmental advocacy groups, community or neighborhood groups, andaffected or interested individuals. The relationships between these stakeholders can be classifiedinto two types of relationship, which are vertical (hierarchical) and horizontal (parallel) relationshipswith each other [21,22]. Decision-making for individual stakeholders and groups of stakeholders isinfluenced both by the structure of relationships and the characteristics of the individual stakeholders.
Energy related decision-making and policy-making (We use the phrase “energy (and environmental)decision-making and policy-making” or “decision-making in energy sector” interchangeably in this paperbecause the research object is related with both energy and environmental sector.) at the national leveltends to occur in a top-down manner, following the hierarchical structure of the country’s institutions.In the UK, for example, energy decision-making functions have historically been performed mainly by thecentral government and large corporations in the private sector. This situation began to change after theLocalism Bill was stipulated in 2010 aiming to shift decision-making power from central governments toindividuals, communities, and local government [23,24]. Another example is from a developing country,Indonesia, where for more than two decades since the first national energy policy was introduced in1981, the key strategic energy decisions and policies are made centrally by the national government .The role of local government in the energy sector was recognized after the promulgation of the EnergyAct in 2007. The act mandates each local government to formulate its own local energy masterplan,based on the targets outlined by the national energy masterplan.
Recent experiences from both countries have shown that the local authorities are mandatedand expected to have more capacity in energy decision-making functions. The long period ofcentralized energy decision-making experience in both countries has created a great challenge forthe local authorities to pick up the task. Lack of capacity of the local government with regards toenergy planning, and limited guidelines on how to formulate the masterplan itself, are some ofthe challenges faced by the locals. Despite the limited capacity and experience, local governmentsaround the world have developed various energy-environmental measures and local action plans, as a
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form of participation in global initiatives such as the International Council for Local EnvironmentalInitiatives (ICLEI) and the Climate Alliance . Aside from having a mandate to implementenergy-environmental measures at the local level, local authorities are also expected to involve andnurture the community or grassroots levels in local energy initiatives .
Energy decision-making functions at the community level have been empirically observed inNorth America [28–30]. Most of the decisions and measures taken are on climate change mitigationplanning, considered as the re-emergence of the energy planning efforts which increased after the oilcrisis in the 1970s, but later declined in the 1980s due to lower energy prices . Although the numberof local actions for energy measures in USA were increased after 2006, all of the decision-makingprocesses identified were initially developed at the municipality level first . The municipalities theninvolved the community in their plans to reduce community-wide energy use and GHG emissions.Although similar, the Canadian experience with its community energy management or communityenergy planning program is slightly different from what happened in the USA. Observations of theCommunity Energy Plans (CEPs) that emerged during 2003–2007 [29,30] have shown the potentialof community roles in formulating action plans specifically related to energy efficiency, energyconservation, and application of renewable energies . However, since CEP is part of a broadercommitment of the municipalities on forming local action plans for GHG reduction, the content ofthe CEP is often written in accordance to what the municipality or municipal council needs .These practices are somewhat different from what was conceived by Jaccard, et al.  as communityenergy management.
The practices of energy related decision-making at the community level is also evident in Europeancountries, such as in the UK and Germany [27,32,33]. Often referred to as grassroots initiatives [27,34]or community (renewable) energy [33,35], it is defined as projects where communities exhibit a highdegree of ownership and control, and collectively benefit from the outcomes . The term communityin this literature is relatively broad, referring to a group of people who share the same geographicallocation (neighborhood communities) or the same interest (non-governmental organizations) .The recent practices of community energy in Europe are gradually shifting as part of socio-politicalmovements from the grassroots level  and, thus, they are more likely to be considered as bottom-upinitiatives when compared to the CEPs in North America.
Energy decision-making at the individual level is traditionally studied as a part of consumerbehavior studies which view the individual as the energy customer or end-user [36,37]. Individualsas consumers make everyday decisions related to energy; therefore, they are becoming the target ofvarious energy measures , such as the behavior change programs in energy consumption andenergy technology adoption . The high potential of new energy systems and technologies such asrenewable energy systems and smart grids have shifted the focus of individual energy decision-making.In the light of these technologies, individuals’ energy decisions are not only shaped by the energysystem and policy, but can also shape the system . The social foundation of smart grids consistsof “decentralized socio-technical networks that underpin the electricity consumption of groups ofconsumers who are increasingly becoming autonomous” . However, for effective technologyadoption, it is suggested to no longer view the individual solely as a consumer of energy, but also as acitizen, part of a community or society .
From the research related with energy decision-making above, it is found that energydecision-making functions occur at various stakeholder levels, and the decisions made by onestakeholder may affect others in the total energy system. The challenge of shifting towards a morelocalized and distributed energy system creates a need for every stakeholder not only to activelyparticipate in energy decision-making, but also to become more autonomous.
2.4. Definition of Autonomous Decision-Making
In this research, we put more focus on autonomy as the property of persons, not as a property ofthe system or environment. This study considers that each decision-maker is seen as an autonomous
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system, or in other words, autonomy is a property of each stakeholder who participates in thedecision-making process. This means that every decision-maker or stakeholder has their own goal toachieve and has the autonomy to decide by themselves. Thus, as mentioned earlier, this study viewsthat autonomy can exist in both centralized and decentralized approaches.
In light of this, we define the autonomous decision-making as the process where decision-makershave the freedom and ability to find problems, select goals, and make decisions for achieving theselected problems/goals by themselves in a responsible manner based on available information.It follows that persons having the ability to self-determine, self-govern, show self-control, andself-learning are persons who exhibit autonomous behavior. The definition for each autonomousbehavior used in this paper is presented in Table A1 in the Appendix.
3. Methodology for Modeling an Autonomous Decision-Making Process
The aim of this study is to develop the autonomous decision-making model for the energyand environmental management process by using Petri-net. For this aim, an energy-environmentalmanagement project in Indonesian community (Rukun Warga) is selected as a case study. The stepsperformed for modeling in this paper are: (1) case selection and data collection; (2) decomposingthe decision-making process and extraction of the stakeholders’ properties; (3) identification ofstakeholders’ autonomous properties; and (4) modeling the decision-making process from the observedcase using Petri-net and analysis of the model.
3.1. Case Study Selection and Data Collection
This paper undertook one decision-making process as a case study to be modeled, and there is astrong indication to select this particular case. The selected case study was included and investigatedalong with other five community decision-making processes in our previous work . These caseswere, in turn, selected from a broader set of around 20 case studies. The five cases were selected due totheir success in project implementation and the availability of detailed documentation and information.Among the five cases, the community presented in this study was considered to have utilized bothcentralized (top-down) and decentralized (bottom-up) decision-making approaches. Since we arguedthat autonomous decision-making can occur under both approaches, by selecting this case we caninvestigate and model autonomous decision-making under both approaches using the same case.In addition to that, by using the same case study which exhibits two different decision-makingapproaches over a period of time, the behavior change and improved capability of the community inmaking decision were observed.
The model developed here is based on a case study of a practical decision-making process for awaste management system project in a community in Bandung City, Indonesia. The waste managementtechnique utilized in the community project is a bio-digester installation to transform household wasteto energy (biogas). This case was selected because a considerable number of stakeholders wereinvolved in the activities with relatively even inputs to the project. Various stakeholders’ involvementin a project is a rare occasion, especially when almost all stakeholders can contribute relatively evenlyin the project. This situation occurred because the project developed in two phases. The first phasestarted as one project and then changed to another project after the first went through a stagnant phase.The second phase achieved quite a successful outcome and is still in operation at the time of writing.The stakeholders that were involved in each phase are different, which is one reason why there werevarious stakeholder contributions. This unique situation is considered useful for understanding thepossible outcomes from various stakeholders’ engagement when the project changed course.
A thorough data collection is necessary for understanding the case study well. Informationabout the community activities and decision-making process were collected using secondary andprimary sources. Various secondary records used were project reports, academic reports, journalarticles, newspaper articles, and web-based articles. Interviews, informal discussions, observation, anddemonstration of the biogas installation were also undertaken during site visits. The primary sources
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interviewed are the chief of the community, the former community chief, bio-digester operators, andthe recycling center operator.
3.2. Decomposing the Decision-Making Process and Extraction of the Stakeholders’ Properties
The decomposition of the case study is important for constructing the autonomousdecision-making model as a discrete event system. There are two steps involved in this decomposition,which yield two major results that become the foundation of the discrete event system for modelingautonomous decision-making. Firstly, the community decision-making process is decomposed intodecision-making stages. Secondly, the properties of each stakeholder involved in each stage areidentified. Utilizing the framework developed in our previous work , the decision-making processis decomposed. Modified from Simon , Huber , and Petrie , the framework consists of fourimportant phases, namely: (1) problem finding; (2) knowledge and information; (3) consensus building;and (4) decision and implementation (see Figure 2). The points or questions in each phase function asguidance in decomposing decision-making stages and identifying the stakeholders’ involvement.
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sources interviewed are the chief of the community, the former community chief, bio-digester
operators, and the recycling center operator.
3.2. Decomposing the Decision-Making Process and Extraction of the Stakeholders’ Properties
The decomposition of the case study is important for constructing the autonomous
decision-making model as a discrete event system. There are two steps involved in this
decomposition, which yield two major results that become the foundation of the discrete event
system for modeling autonomous decision-making. Firstly, the community decision-making process
is decomposed into decision-making stages. Secondly, the properties of each stakeholder involved in
each stage are identified. Utilizing the framework developed in our previous work , the
decision-making process is decomposed. Modified from Simon , Huber , and Petrie , the
framework consists of four important phases, namely: (1) problem finding; (2) knowledge and
information; (3) consensus building; and (4) decision and implementation (see Figure 2). The points
or questions in each phase function as guidance in decomposing decision-making stages and
identifying the stakeholders’ involvement.
Figure 2. The decision-making decomposition framework .
The procedure for extraction of stakeholders’ general properties was performed based on our
previous work which utilized five case studies of community energy-environmental projects, of
which the present case study was one . The five different cases of community projects selected
(from a set of around 20) exhibit various types of decision-making processes, ranging from
centralized to decentralized approaches. All of the five cases were considered as successful in
reaching the project goals. From analysis of these successful cases, the role and properties of the
stakeholders’ are extracted by utilizing the framework in Figure 2, with the properties and the
framework development itself based on the decision-making literature.
Figure 2. The decision-making decomposition framework .
The procedure for extraction of stakeholders’ general properties was performed based on ourprevious work which utilized five case studies of community energy-environmental projects, of whichthe present case study was one . The five different cases of community projects selected (froma set of around 20) exhibit various types of decision-making processes, ranging from centralized todecentralized approaches. All of the five cases were considered as successful in reaching the projectgoals. From analysis of these successful cases, the role and properties of the stakeholders’ are extractedby utilizing the framework in Figure 2, with the properties and the framework development itselfbased on the decision-making literature.
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3.3. Identifying the Stakeholders’ Autonomous Properties
The structured method for identifying the stakeholders’ autonomous properties from thestakeholders’ general properties is explained here. The list of stakeholders’ general properties whichcontributed to the success of the community project was derived from a thorough literature review intodecision-making processes, cross-checked with successful case studies. In order to determine which ofthese properties are aligned with autonomy in decision-making, a further analysis was undertaken.The decision-making process, as a whole, is considered to be autonomous decision-making if thestakeholders in the system are making decisions autonomously. In other words, the stakeholders needto exhibit an autonomous behavior. Therefore, the stakeholders’ autonomous properties are identifiedby cross-comparing the stakeholders’ general properties with elements of autonomous behavior.
The cross-comparison process was performed qualitatively using content analysis of theautonomous behaviors and stakeholders’ properties definitions. The stakeholders’ general propertiesare identified in the previous step, while the elements of autonomous behavior are identified inSection 2, namely: (1) self-governance; (2) self-control; (3) self-learning; and (4) self-determination.Upon defining each stakeholders’ property and autonomous behavior, each property is examined.Those which comply with at least one definition of autonomous behavior are identified as stakeholders’autonomous properties. Utilizing this method, the stakeholders’ autonomous properties can beobjectively identified.
3.4. Developing and Analyzing the Autonomous Decision-Making Model Using Petri-Net
The method for constructing the autonomous decision-making process using Petri-net is presentedin this section. The justification of Petri-net utilization is explained, followed by the Petri-net historyand its utilization. Afterwards, a brief explanation of a standard Petri-net model. The autonomousdecision-making model developed in this paper is built as a discrete event system by compiling theresults from previous steps, which are the decision stages and the stakeholders’ properties. The methodto represents the discrete events into a Petri-net model is also explained in this section.
In this paper, we consider the decision-making process as a system built upon discrete eventswhich perform and interact with each other sequentially and in parallel. Energy-environmentaldecision-making is of public interest, therefore the decision-making involves many and variousstakeholders. In our research, the stakeholders are autonomous. They are being shaped by, and can alsoshape, the system. Therefore, an interrelated bi-directional connection between stakeholders and thedecision-making process is expected. Petri-net has an advantage of representing the model in two-ways:graphically and mathematically. Therefore, we consider that Petri-net is a suitable tool to represent thecomplexity of multiple autonomous stakeholders in energy-environmental decision-making. Moreover,the utilization of Petri-net enables a simple simulation of autonomous decision-making model to beperformed further.
Petri-net is one of the tools often utilized for modeling a discrete event system, and nowadays itsapplication has been employed on a very broad field of study, including decision-making. The historyof Petri-net is established by its development by Carl Adam Petri in 1962. Petri-net is useful formodeling the flow of information and control in systems, especially those which exhibit asynchronousand concurrent events [42–44]. Petri-net is commonly applied to model various kinds of dynamicdiscrete-event systems such as computer networks, manufacturing plants, communication systems,logistic networks, and command and control systems . In recent years, the utilization of Petri-nethas reached far beyond computer science and manufacturing studies. For example, Petri-net hasbeen used to model decision-making processes in a legal case  and modeling the story plotfor games [47,48]. In the energy-environmental field, several studies have employed Petri-net inmodeling: a more energy efficient machine tool , multisource energy conversion systems ,energy management system for autonomous micro-grids , municipal waste management , andenvironmental effects of biofuel utilization . The advantage of utilizing Petri-net in this study isthat it can describe objectively a decision-making process with multi-stakeholder involvement.
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A standard Petri-net consists of P, T, I, O, µ (places, transitions, inputs, outputs, marking/token).In detail, P is a finite set of places, which are represented by circles; T is a finite set of transitions, whichare represented by rectangles/bars; I is an input function which represents connection from P to T;O is an output function which represents a connection from T to P; and µ is the initial marking whichis represented by a small dot called a token .
In order to transform the discrete events of autonomous decision-making into Petri-netaccordingly, the results from previous steps are compiled. First, the result from decomposing thedecision-making process are the decision stages. These decision-making stages, which are consideredas discrete events, are transformed into “transitions” in the Petri-net model, whereas the result fromthe autonomous properties extraction is the stakeholders’ properties. The state or the combinations ofthe stakeholders’ properties, are represented as “places”. Likewise, the results or outputs from eachevent/stage are also represented as “places”. The relationship between the state and the stages arerepresented with inbound and outbound arcs. In short, the decision-making stages can be transformedinto the Petri-net by:
1. Describing the state of affairs or a condition experienced by the stakeholder as a Place (P).2. Describing the decision-making process, or event, or action conducted by the stakeholder as a
Transition (T).3. Describing the relationship of Place(s) and Transition(s) and the movement of the token (µ) with
inbound and outbound arcs.
The token moves from one place to another by “firing” through a transition. A place has a tokenif a particular stakeholders’ condition or property is satisfied, thus firing the transition. The existenceor the absence of the condition is the key factor that determines whether a transition in the Petri-net isenabled or not.
The decision-making model is constructed by combining all of the transitions and placesrepresenting the decision-making stages into one Petri-net model. For simplification purposes, severaldecision stages are represented as simple Petri-net models, which are drawn hierarchically in anotherlayer under the main model. These lower layers of Petri-net models do not affect the purpose of thewhole model, which tries to show the relationship between stakeholders’ autonomous properties ineach decision-making stage and decision outcomes.
The utilization of Petri-net to describe the decision-making process made the autonomous part ofthe decision-making more prominent and easier to be identified. Therefore, we can identify the criticalpoints in the decision-making process, where the existence or absence of autonomous properties willlead to a different decision or achieve different outcomes. The performance of autonomous propertiesin the success of the decision-making is going to be evaluated by analyzing the combinations of theconditions resulting from the simulation.
This section presents the results obtained from each method aforementioned. A brief descriptionof the selected case study is presented prior to the results from decomposing the case studydecision-making process into stages. The stakeholders’ autonomous properties are identifiedafterwards. Later on, the development and analysis of the decision-making model using Petri-netare explained.
4.1. Overview of the Case Study
As described earlier, the case study project had two phases, and each phases is briefly explainedhere. The initial project was called a Community-based Basic Infrastructure Improvement Program(CBIIP), with the final goal to improve the sanitation situation in the community. The case study consistsof two related projects, which are a composting center and bio-digester installation. The bio-digester
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installation project was an improvement to an existing composting project in the RW 11 community (RWare often identified by number. RW 11 means it is the 11th community to exist in the particular village).This community, inhabited by 3000 people, or roughly 800 households, is one of the low-income slumareas in Bandung. It is one of the densest districts in the city. Recognizing the need for improvementof community life, CBIIP was initiated by the Ministry of Public Works in the Bandung Branchwith assistance from the Bandung City government in 1996. Focusing on economic, social, andenvironmental aspects, one of the projects conducted was the construction of a composting centerlocated in RW 11 to improve the poor sanitation and waste situation . After the project term wasfinished and the budget terminated, the composting center operation became stagnant, and was thenreplaced by a bio-digester installation.
The second project, a biogas production project in the form of a bio-methane digester installation,was initiated by the community in collaboration with academicians, the private sector, andcommunity-based organizations (CBO). After the composting system was not as successful as planned,especially in terms of profit, it was terminated around 2009–2010. However, views on waste andgarbage in the RW 11 community had changed. They maintained the waste segregation activities, andthe women’s organization (My Darling) began selling plastic waste and tried to reuse it for handicrafts.Moreover, the existing CBO tried to seek financial support by submitting proposals to international andnational non-governmental organizations (NGOs) . Eventually, with assistance and consultationfrom academic scholars, the Environmental Agency and a local NGO, and financial help from the localbank, the composting system was changed to the bio-methane system, which produces biogas forhouseholds and liquid fertilizer.
One recent study about the biogas production in this community has been conductedthoroughly . The outcomes from the biogas production project were studied from socio-economicperspectives. It was found that the biogas production at RW 11 is currently not economically feasibledue to limited market reach for the bio-slurry products. Meanwhile from the social point of view, thestudy identified that the community was relatively accepting of the project despite a mix of responsesfound among RW 11 community members. It can be concluded that this pilot project in biogasproduction is still operating because of the social acceptance factors rather than economic factors.
4.2. The Decision-Making Stages and Stakeholders’ Properties
The decomposition of the decision-making process resulted into two major outputs. The firstoutput are the decision-making stages, and the second are the stakeholders’ general properties. Theseoutputs are the foundation in establishing an autonomous decision-making model as a discrete eventsystem. The case history and other related information obtained from various sources are analyzedqualitatively to decompose the decision-making process of the case study into decision stages. Utilizingthe framework in Figure 2, we decomposed the decision-making process of the biogas productionproject in RW 11 into six stages, which are:
1. Find or define the problem2. Design the solution alternatives3. Agreement/consensus building4. Implementation and construction of the Waste Management System (WMS)5. Management (O and M)6. Termination of the project
Even though the framework suggested four major phases, the number of stages drawn in thePetri-net model may vary and, thereby, be more than four. The biogas production project in RW 11has been established for a long time, therefore, it has been gone through the stages of “management”and “project termination”. Moreover, the project has been regenerated into another project, which isstill running. Depending on the case study, the decomposition of the decision-making process may
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result in various numbers of stages. These stages are represented in the Petri-net model as transitions.The relationships between each transition are drawn by combining it with the stakeholders’ properties.
As mentioned in Section 3.2, the stakeholders’ properties are extracted from the successful casestudies by applying the same framework (Figure 2) and based on literature on various decision-makingprocesses. These properties were taken from various energy-environmental decision-making studies,as presented in Table A2 in the Appendix. This process resulted in the stakeholders’ generalproperties, listed in Table 1. These extracted properties are considered to be those which contributedto successful community decision-making implementation. The stakeholders’ general propertiesare further examined using autonomous behavior elements in Section 2 to identify the stakeholders’autonomous properties.
Table 1. Stakeholders’ general properties.
Stakeholders’ General Properties
1 Self-control 11 Trust2 Initiative 12 Interaction3 Self-learning 13 Collaboration4 Motivation 14 Openness5 Ability to organize 15 Commitment6 Leadership 16 Local culture7 Self-governance 17 Networking ability
8 Ability to collect andunderstand information 18 Creativity
9 Communication ability 19 Innovativeness10 Responsibility 20 Proximity
4.3. The Stakeholders’ Autonomous Properties
Identification of the stakeholders’ autonomous properties is one of the important processconducted in this paper. The properties extracted in previous step are general stakeholder propertiesthat contributed to the success of the project goal. These properties are cross-compared with theautonomous behaviors mentioned in Section 2. Among the 20 general properties listed above, three ofthem are already included as autonomous behaviors (self-control, self-learning, and self-governance).The remaining 17 properties were cross-checked with the autonomous behaviors.
The method for identifying stakeholders’ autonomous properties explained in Section 3.3 requireseach autonomous behavior and the general properties listed in Table 1 to be clearly defined. Fromthese definitions (see Appendix, Tables A1 and A2), the stakeholders’ general properties are objectivelyidentified as to which autonomous behavior they exhibit (if any). The results of this cross-comparisonare presented in Table 2.
From Table 2, eleven out of seventeen properties are considered as exhibiting stakeholders’autonomous behavior. The other six are not marked as autonomous behavior of the stakeholder, for atleast two reasons. First, they are not a property of persons or individuals. The properties, such as localculture, trust, and proximity are categorized as a system or environment property. Therefore, eventhough they exhibit some traits of autonomy, they are not included as stakeholder properties. Second,the properties of creativity and innovativeness, by definition, are not regarded as corresponding withthe autonomy definition or dimensions.
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Table 2. Extraction of Stakeholders’ autonomous properties.
Self-Governance Self-Control Self-Learning Self-Determination
1 Initiative X X X O
2 Motivation X X X O
3 Ability to organize O O X X
4 Leadership O O X X
5 Ability to collect andunderstand information X X O X
6 Communication ability O X X X
7 Responsibility X O X O
8 Trust X X X X
9 Interaction X X O X
10 Collaboration X X O X
11 Openness X X X X
12 Commitment X O X O
13 Local culture X X X X
14 Networking ability X X O X
15 Creativity X X X X
16 Innovativeness X X X X
17 Proximity X X X X
After correlating these properties with the autonomy dimensions, selected properties are furtherclassified into seven points based on definitional similarity, and they are as follows:
1. Motivation, initiative; selected because the decision-makers need to have motivation or initiative,or ability to think by themselves in order to be considered as autonomous.
2. Leadership, ability to organize; selected because autonomy also requires self-governance andself-control. In order to have the ability to govern or organize themselves, the decision-makersneed to have some level of leadership and ability to coordinate and communicate their goal withtheir subordinates or members.
3. Self-learning, ability to manage information; selected because an autonomous decision-maker needsto have the willingness and ability to learn, to manage and collect information, and to understandthe information necessary to make decisions.
4. Interaction between the community members; one of the results of the analysis conducted on thefive cases was that the interaction among community leaders and members has an important rolein reaching a consensus or decision, as well as in decision implementation, and sustaining theoperation and maintenance of the project. A decision that is reached through group interactionperforms better when compared to a decision reached by a group of people that does not interactat all .
5. Networking and collaboration between stakeholders; this property is linked with the previousproperty. We differentiate it because, in this property, the community (leaders and members) isconsidered as one stakeholder. The networking and collaboration between the community andother stakeholders outside the community, such as government agencies, officials, local NGOs,private sectors, and others, was seen in the five cases and contributed to the success of the project.
6. Persuasion and negotiation ability; this property is closely related with the leadership level of thestakeholder. This property was also very useful in reaching a consensus or decision, especially
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when the project involved multiple stakeholders. This property is found predominantly in thecases where the initiative does not come from governments.
7. Responsibility and commitment; this property is especially important when the decision is ready tobe implemented. In order for the project to be constructed, each stakeholder involved needs to beresponsible for their duty and commit to the decision that has been made.
4.4. The Petri-Net Model
The autonomous decision-making model, which in this paper is regarded as a discrete eventsystem, is represented using Petri-net. The principal process of modeling the decision-making intoPetri-net can be described as follows. Each stage of the decision-making in Section 4.2 is transformedinto transition for the Petri-net model. For graphical purpose, we provide two version of Petri-netgraphs. The simplified Petri-net diagram for this case study is presented in Figure 3, meanwhile thecomplete Petri-net graph using Yasper is presented in Figure A1 in the Appendix.
The conditions for each stakeholder involved are given at the beginning of the net, and are notchanged during the course of the process. There are three subnets added (for detail see Appendix,Figure A2–A4). Each subnet is designed for one autonomous property, namely the Motivation Subnet(T1), the Leadership Subnet (T2), and the ability to manage information, shortened as the InformationSubnet (T3). The reason behind the subnets’ creation is because the model will be simulated bychanging the number of stakeholders involved and changing the combination of their properties.Therefore, it is important to show the process of how each stakeholder becomes autonomous ornon-autonomous in detail. However, we realized that this process can make the whole decision-makingprocess model more visually complex. Therefore, we added several subnets in the Petri-net model,hence, making it a hierarchical model. The subnets show the process of every stakeholder in becomingautonomous or non-autonomous. Autonomous stakeholders will have a token in the correspondingplaces, whereas those who do not have autonomous properties will have no token. The other reason isbecause the content of these hierarchical transitions are fluid, depending on how many stakeholdersare involved. This makes it inefficient to draw directly on the primary layer.
The results from these hierarchical transitions from the subnets are shown on the primary layer asone single place, which is a simplification of the number of places corresponding to each stakeholderinvolved (in Figure 3, these are designated by blue coloring, hereafter they are called “blue” places).If the number of stakeholders is more than one, then each blue place consists of a combination ofstakeholders’ conditions. This simplification is purely for graphical purposes. These blue places aredrawn as several single places in the complete version of the Petri-net model. The example givenin Figure A1 of the Appendix shows that if there are five autonomous stakeholders involved in thedecision-making process, this would result in each of the hierarchical transitions (T1, T2, and T3)producing five tokens in each of the corresponding places. Therefore, each blue place in primary layer(P2, P3, and P4) actually consists of five single places with a token in it. For simulation purposes, itis not possible to simply put five tokens in each of P2, P3, and P4. This is because at later transitions(T6 and T8), the rules are specifically differentiated based on the stakeholder types. Decision-makingprocesses may require certain specific stakeholders to make an autonomous decision—in this casea token from these stakeholders will be compulsory. In addition to this, the specific direction thata decision takes may be designated by which, or how many, other stakeholders have autonomousproperties (a token, in this case).
In Figure 3, there are three variations of transition. First, is the standard transition, which ismarked by a black box. Second, the orange diamond-shape transition, which represents an XORtransition. An XOR transition consumes one token from one of its input places and produces a tokenin one of its output places. This means that this transition can be fired if there is at least one token inone of its input places. The third transition is a hierarchical transition (T1–T3). As mentioned before,the Petri-net model in this paper is a hierarchical one, meaning there is another process or another set
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of Petri-net models under the primary layer. A detail explanation on the variations of transitions withthe corresponding decision type used in this paper is presented in Table 3.
There are three other important elements of the Petri-net model shown in Figure 3, namelythe inbound arcs, outbound arcs, and tokens. The inbound and outbound arcs between places andtransitions show the direction of token movement. In addition, they also show the relationshipbetween places and transitions. Since places represent the conditions needing to be fulfilled for firingthe transitions, it is easy to identify what kind of conditions are required for an action or event to occur.The bidirectional arc represents a simplification of a situation in which whenever a transition is fired,then the transition will produce a token in the output place and also put the token back in the inputplace. The legend for the Petri-net in Figure 3 is presented in Table 4.
The Petri-net developed in this paper shows that the discrete event system consists ofdecision-making stages and the role of stakeholders involved in a decision-making process can beobjectively and logically modeled. Utilizing the procedures explained above, other decision-makingcases can also be represented using Petri-net. Although the model might be different in detail, thedecision-making stages are relatively similar.
Table 3. Type of transitions with its corresponding decisions type.
Type of Transition Type of Decision
Standard transition Used if the condition(s) to reach a particular action/decision is unnegotiable, or if thenumber of states resulted from a particular action/decision are definite.
XOR transitionUsed if there are two or more states that possible as inputs or outputs of the particularaction/decision. This type of transition is usually applied to decisions that branchessubject to certain conditions.
Hierarchical transition Used as a representative of a sub-layer in the Petri-net. The sub-layer contains another setof transitions-places which is deliberately hidden to simplify the main Petri-net model.
Table 4. Legend for places and transitions in the Petri-net model.
Place Description Transition Description
P1: Waste and sanitation problem situation T1: Motivation subnet
P2: Set of stakeholders’ motivation level T2: Information subnet
P3: Set of stakeholders’ ability to manageinformation level T3: Leadership subnet
P4: Set of stakeholders’ leadership level T4: Problem finding process
P5: Problem is defined T5: Designing alternatives process
P6: Alternatives are designed T6: Decision-making
P7: WMS technique is selected(decision is made) T7: Construction of WMS
P8: WMS is constructed(decision is implemented) T8: Operation & Maintenance
P9: Waste is reduced T9: Termination of the project
P10: Project stopped
P11: Project continued
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Figure 3. Hierarchical Petri-net (simplified) describing the decision-making process of a WMS case study. Figure 3. Hierarchical Petri-net (simplified) describing the decision-making process of a WMS case study.
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5. Analysis and Discussions
Two main steps were performed through the methods explained in this paper. First is the methodto decompose the community decision-making process into discrete events. This process resulted intotwo outputs, which are the decision-making stages (Section 4.2) and the stakeholders’ autonomousproperties (Section 4.3). The second step is the method to build the discrete event system in the formof the Petri-net model, which generalizes the decision-making in a case of energy and environmentalmanagement (Section 4.4).
The decomposition process produces decision-making stages and the stakeholders’ autonomousproperties. The decision-making stages are performed utilizing the framework in Figure 2. As a result,six decision-making stages are obtained; namely, problem definition, alternatives design, agreement orconsensus building, implementation and construction, management, and project termination, whereasthe stakeholders’ properties are identified by qualitatively cross-comparing the stakeholders’ generalproperties with the autonomous behaviors. There are seven autonomous properties identified here;namely, (1) motivation and initiative; (2) leadership and ability to organize; (3) self-learning or theability to manage information, (4) interaction; (5) networking and collaboration; (6) persuasion andnegotiation ability; and (7) responsibility and commitment. Among these, properties (4) and (5)are considered as more a property of groups of people, meaning they exist if there are at least twotypes of stakeholders involved in the decision-making, whereas the other properties belong to anindividual stakeholder.
The results from the decomposition process are then represented by Petri-net. The model isconstructed by combining the decision-making stages that already converted into transitions andplaces. Analysis of the Petri-net provide a further understanding that there are several transitions thatwould yield different outcomes if the conditions at the blue places are changed. These transitions areidentified as critical points, which are identified from Petri-net graph in Figure 3.
As discussed in Section 4.4, a blue place contains the result from the hierarchical transitionsand each blue place can represent more than one “standard” place. A token in one of the sub-placescontained in a blue place represents the particular stakeholders’ autonomous properties and it will notbe changed during the course of the simulation. For example, if a stakeholder is set since the beginningas not having motivation properties, then it will continue to lack motivation until the end of the modelor the termination of the model. A critical point in this study refers to a certain transition in the Petri-netmodel that is influenced by the conditions set in the blue places, which have particular influence onautonomy. From the model, the critical points identified in this decision-making process are:
1. Problem finding process (T4). At this critical point, there are two determining properties, whichresulted from motivation subnet (T1) and information management capability (T2). T4 fires ifthere is at least one token in one of its input places (P2 and P3). This means that at this stage, anystakeholder, regardless the type, can contribute in finding the problem as long they have highmotivation or strong leadership.
2. Designing alternatives (T5), fires depending on the property of information managementcapability (T2). T5 fires if there is a token in P5 and there is at least one token in P3. This meansthat in order to design decision alternatives, at least one stakeholder must have the capability tomanage information.
3. Decision-making process or consensus-building process (T6), which is determined by theproperty of leadership level (T3). T6 fires if there is a token in P6 and at least one token inP4. This means that in order to reach a decision or a consensus together, at least one stakeholderneeds to have strong leadership. The output of this transition is differentiated by the specificstakeholders’ conditions.
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4. Operation and maintenance phase (T8), is determined by all three properties of: leadership level(P4), motivation level (P2), and information management capability (P3). Basically, T8 fires ifthere is a combination between the properties of several stakeholders together. This means thatcollaboration, networking, and interaction between stakeholders plays an important role in thisOperation and Maintenance stage. However, since the leadership property (P4) is already givenin T6, therefore there is no need to connect T8 with the inbound arc from P4. The result of T8will be differentiated based on the properties from blue places based on types and propertiesof stakeholders.
5. Termination of the project (T9), determining property: result from the O and M phase (T8).The outputs from the previous transition (T8) are differentiated based on the stakeholder typesand properties. In the simulation, the rule will be imposed on T9 as to whether to produce atoken for P10 or P11, based on the token condition in P9. For example, if the token produced fromT8 shows a condition of autonomous local people (community leaders or interested individuals),then the project will be more likely to go beyond project termination, and therefore T9 willproduce a token in P11. Since T9 is an XOR transition, the firing of T9 can only be produced inone of P10 or P11.
Among these critical points, the first two points (T4 and T5) determine whether the process willreach a decision or fail to reach a decision. The latter three points (T6, T8, and T9) determine the varietyof success levels in achieving the project goal. Meanwhile T7 is not identified as a critical point becausethe transition only depends on one input place.
The Petri-net model in this paper represents the decision-making process as it occurred incommunity case studies. The common traits of community decision-making should not be neglected.For example, in a community, if a certain problem exists and a stakeholder proposes some solutionalternatives but the remaining stakeholders are not able to reach a consensus or decision, then thewhole decision-making process fails/stagnates and the problem will persist. This means that, forsolving the same problem, the decision-making process needs to be started from the beginning again.In the simulation, this trait will be represented by the instant termination of the model simulationevery time a transition is not fired.
The critical points in this paper are identified by developing the Petri-net model which resultedfrom carefully decomposing the case study. Therefore, the most important part is decomposing thecase’s story into decision-making stages, which can only be performed if the case study or projecthistory is well understood. This made the data collection procedure holds an important role inunderstanding the context under which the decision was taken. Although complete information mightbe available in the form of reports and secondary records, direct field visits and observations are highlyrecommended to obtain a thorough understanding of the targeted community, and also to avoid biasfrom previous researchers. Another important point is the selection of principal informants to beinterviewed. It is best to interview stakeholders that are involved directly at the beginning of theproject even though they might already be very old or have already stepped down from their positionif the project has been conducted for a number of years.
The method explained throughout this paper comprises of decomposing the communitydecision-making process, extraction of the stakeholders’ autonomous properties, and modeling theautonomous decision-making process. The results of these steps are complemented by the resultsobtained from various literature and data collection. The structured method utilized in this paper canbe summarized in Figure 4 below.
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Figure 4. General method for developing autonomous decision-making model.
This paper presents a method to build an autonomous decision-making model, which is
considered to be important within the development of decentralized generation and
demand-centered projects in energy and environmental beneficiation. However, the approach is
generalizable to other fields and case studies beyond that presented here. The specific Petri-net must
be designed given the understanding of the stakeholders and project elements involved in decision
making, which is performed by decomposing a decision-making process into discrete events or
decision-making stages as shown in Figure 4. Using Petri-net as a tool, the decision-making stages
are transformed into a set of place-transitions or simple Petri-net models, and these small models are
compiled to construct the autonomous decision-making model. The utilization of Petri-net to
represent decision-making models helps the decision-making process to be analyzed objectively and
important stages of autonomous decision-making are prominently shown. These important stages
are identified as critical points of autonomous decision-making. A critical point is influenced by the
stakeholders’ properties and determines the output of the model, or whether the model can reach
the end of the network or not.
The results of this paper are going to be further employed for simulations based on the Petri-net
model. The utilization of Petri-net in building the autonomous decision-making model is considered
as one of the effective ways to perform the model simulation in the future study. Some of the
stakeholders’ autonomous properties identified above such as motivation, leadership, and ability to
manage information, are going to be assigned deterministically to each stakeholder involved in the
decision-making process and various outcomes from the simulations will be observed in order to
identify the key conditions suitable for successfully achieving goals.
Acknowledgments: The authors are grateful for the comments and suggestions from three anonymous
reviewers. The first author would like to extend her gratitude to the Ministry of Education, Culture, Sports,
Science and Technology (MEXT), Japan, for supporting this study.
Author Contributions: The manuscript is prepared by Niken Prilandita, under the supervision of Tetsuo Tezuka
and Benjamin McLellan, who assisted in co-authoring and improving paper.
Conflicts of Interest: The authors declare no conflict of interest.
Figure 4. General method for developing autonomous decision-making model.
This paper presents a method to build an autonomous decision-making model, which isconsidered to be important within the development of decentralized generation and demand-centeredprojects in energy and environmental beneficiation. However, the approach is generalizable to otherfields and case studies beyond that presented here. The specific Petri-net must be designed giventhe understanding of the stakeholders and project elements involved in decision making, which isperformed by decomposing a decision-making process into discrete events or decision-making stagesas shown in Figure 4. Using Petri-net as a tool, the decision-making stages are transformed into a setof place-transitions or simple Petri-net models, and these small models are compiled to construct theautonomous decision-making model. The utilization of Petri-net to represent decision-making modelshelps the decision-making process to be analyzed objectively and important stages of autonomousdecision-making are prominently shown. These important stages are identified as critical pointsof autonomous decision-making. A critical point is influenced by the stakeholders’ properties anddetermines the output of the model, or whether the model can reach the end of the network or not.
The results of this paper are going to be further employed for simulations based on the Petri-netmodel. The utilization of Petri-net in building the autonomous decision-making model is consideredas one of the effective ways to perform the model simulation in the future study. Some of thestakeholders’ autonomous properties identified above such as motivation, leadership, and ability tomanage information, are going to be assigned deterministically to each stakeholder involved in thedecision-making process and various outcomes from the simulations will be observed in order toidentify the key conditions suitable for successfully achieving goals.
Acknowledgments: The authors are grateful for the comments and suggestions from three anonymous reviewers.The first author would like to extend her gratitude to the Ministry of Education, Culture, Sports, Science andTechnology (MEXT), Japan, for supporting this study.
Author Contributions: The manuscript is prepared by Niken Prilandita, under the supervision of Tetsuo Tezukaand Benjamin McLellan, who assisted in co-authoring and improving paper.
Conflicts of Interest: The authors declare no conflict of interest.
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Table A1 below listed the behaviors or characteristics that commonly associated with autonomousindividual, or even used to define the concept of autonomy. The second column shows that thesebehaviors appeared or even mentioned as a prerequisite for succeeding a decision implementationbased on various literature in decision-making, especially in the energy and environmental sector.The third column contain general definition of each behavior, which are useful for the cross-comparingprocess in Section 4.3.
Table A1. Definition of elements of autonomous behavior.
Autonomous Behavior Definition Mentioned in
Governance refers to the processes of interaction anddecision-making among the actors involved in a collectiveproblem that lead to the creation, reinforcement, orreproduction of social norms and institutions . Therefore,self-governance defined as the capability of an individual orgroup to develop their own way to establish the governanceand running it without intervention.
Refers to a set of processes that enable individuals to guidetheir goal directed activities over time and across changingcontexts . Often used interchangeably withself-regulation .
The capability to perform the act of learning by oneself.Learning here defined as the acquisition of knowledge and/orskills that serve as an enduring platform for adaptivedevelopment and to comprehend and navigate novelproblems .
The capacity to choose and to have those choices, rather thanreinforcement contingencies, drives, or any other forces orpressures, be the determinants of one’s actions.Self-determination often involves controlling one’senvironment or one’s outcomes, but it may also involvechoosing to give up control .
Table A2 below are the observed stakeholders’ properties existed in the successful cases ofcommunity project in energy-environmental management. The second column shows that theseproperties appeared or even mentioned as a prerequisite for succeeding a decision implementationbased on various literature in decision-making, especially in the energy and environmental sector.The third column contain general definition of each properties, which are useful for the cross-comparingprocess in Section 4.3.
Table A2. Definition of stakeholders’ general properties.
Properties Mentioned in Definition
Initiative Behavior characterized by self-starting nature, proactive approach, andby being persistent in overcoming difficulties that arise in the pursuit ofa goal .
Motivation [3,4]Refers to the set of psychological processes governing the direction,intensity, and persistence of actions that are not due solely tooverwhelming environmental demands that coerce or force action .
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Table A2. Cont.
Properties Mentioned in Definition
Initiative Behavior characterized by self-starting nature, proactive approach, andby being persistent in overcoming difficulties that arise in the pursuit ofa goal .
Motivation [3,4]Refers to the set of psychological processes governing the direction,intensity, and persistence of actions that are not due solely tooverwhelming environmental demands that coerce or force action .
Ability toorganize  Capacity to coordinate, manage, facilitate, a particular object/tasks
among group of people to reach a certain goal .
Leadership [66,67]A set of role behaviors by individuals in the context of the group ororganization to which they belong.
The exercise of influence over others by utilizing various bases of socialpower, tactics, and so on in order to elicit the group members’compliance with certain norms and their commitment to achieve thegroup’s objectives .
Ability tocollect andunderstandinformation
 Capacity to collect and understand information without help fromother parties.
Capacity to exchange in exchange information, form understandings,coordinate activities, exercise influence, socialize, and generate andmaintain systems of beliefs, symbols, and values among members ofinstitution/organizations .
An attribute that an adult person is duty-bound to undertake .In environmental behavior, it defined as an individual sense ofobligation or duty to take measures against environmentaldegradation .
Trust A generalized expectancy held by an individual or group that the word,promise, verbal, or written statement of another individual or groupcan be relied on .
A particular kinds of social relationship that are different from, butconstitutive of, groups, organizations, and networks. Interaction occurswhen two or more participants are in each other’s perceptual range andorient to each other through their action and activity .
Collaboration [76,77]Collective action or effort performed by a group of people to solveproblem or adjust environments in order to discover new mutuallybeneficial options .
Openness  Referred as transparency to access information within organization,institution, or society 
Commitment [79,80] Referred as the level of identification with, and attachment and loyaltyto, an organization, an occupation, or some other feature of work .
Local culture  Some shared set of characteristics in common to a particular group ofpeople .
Capacity to perform a process of contacting and being contacted bypeople in one’s social or technical/professional world and maintainingthese linkages and relationships .
Creativity [4,82,83] The generation of ideas or products that are both novel and appropriate(correct, useful, valuable, or meaningful) .
Innovativeness [83,84] The degree to which an individual is relatively earlier in adopting newideas than the other members of a system .
Proximity [86,87] Referred to the spatial distance or familiarity of a certain object orproblem to a person or group of person.
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Figure A1. The Petri-net model drawn using Yasper (no simplification). Figure A1. The Petri-net model drawn using Yasper (no simplification).
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Figure A2. The “Motivation” Subnet.
Figure A2. The “Motivation” Subnet.Challenges 2016, 7, x 23 of 27
Figure A3. The “Leadership” Subnet.
Figure A4. The “Information” Subnet.
Figure A3. The “Leadership” Subnet.
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Figure A3. The “Leadership” Subnet.
Figure A4. The “Information” Subnet. Figure A4. The “Information” Subnet.
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