Units of meaning.
In general, qualitative analysis begins with organizing data. Large amounts of data need to be stored in smaller and manageable units, which can be retrieved and reviewed easily. To obtain a sense of the whole, analysis starts with reading and rereading the data, looking at themes, emotions and the unexpected, taking into account the overall picture. You immerse yourself in the data. The most widely used procedure is to develop an inductive coding scheme based on actual data [ 11 ]. This is a process of open coding, creating categories and abstraction. In most cases, you do not start with a predefined coding scheme. You describe what is going on in the data. You ask yourself, what is this? What does it stand for? What else is like this? What is this distinct from? Based on this close examination of what emerges from the data you make as many labels as needed. Then, you make a coding sheet, in which you collect the labels and, based on your interpretation, cluster them in preliminary categories. The next step is to order similar or dissimilar categories into broader higher order categories. Each category is named using content-characteristic words. Then, you use abstraction by formulating a general description of the phenomenon under study: subcategories with similar events and information are grouped together as categories and categories are grouped as main categories. During the analysis process, you identify ‘missing analytical information’ and you continue data collection. You reread, recode, re-analyse and re-collect data until your findings provide breadth and depth.
Throughout the qualitative study, you reflect on what you see or do not see in the data. It is common to write ‘analytic memos’ [ 3 ], write-ups or mini-analyses about what you think you are learning during the course of your study, from designing to publishing. They can be a few sentences or pages, whatever is needed to reflect upon: open codes, categories, concepts, and patterns that might be emerging in the data. Memos can contain summaries of major findings and comments and reflections on particular aspects.
In ethnography, analysis begins from the moment that the researcher sets foot in the field. The analysis involves continually looking for patterns in the behaviours and thoughts of the participants in everyday life, in order to obtain an understanding of the culture under study. When comparing one pattern with another and analysing many patterns simultaneously, you may use maps, flow charts, organizational charts and matrices to illustrate the comparisons graphically. The outcome of an ethnographic study is a narrative description of a culture.
In phenomenology, analysis aims to describe and interpret the meaning of an experience, often by identifying essential subordinate and major themes. You search for common themes featuring within an interview and across interviews, sometimes involving the study participants or other experts in the analysis process. The outcome of a phenomenological study is a detailed description of themes that capture the essential meaning of a ‘lived’ experience.
Grounded theory generates a theory that explains how a basic social problem that emerged from the data is processed in a social setting. Grounded theory uses the ‘constant comparison’ method, which involves comparing elements that are present in one data source (e.g., an interview) with elements in another source, to identify commonalities. The steps in the analysis are known as open, axial and selective coding. Throughout the analysis, you document your ideas about the data in methodological and theoretical memos. The outcome of a grounded theory study is a theory.
Descriptive generic qualitative research is defined as research designed to produce a low inference description of a phenomenon [ 12 ]. Although Sandelowski maintains that all research involves interpretation, she has also suggested that qualitative description attempts to minimize inferences made in order to remain ‘closer’ to the original data [ 12 ]. Descriptive generic qualitative research often applies content analysis. Descriptive content analysis studies are not based on a specific qualitative tradition and are varied in their methods of analysis. The analysis of the content aims to identify themes, and patterns within and among these themes. An inductive content analysis [ 11 ] involves breaking down the data into smaller units, coding and naming the units according to the content they present, and grouping the coded material based on shared concepts. They can be represented by clustering in treelike diagrams. A deductive content analysis [ 11 ] uses a theory, theoretical framework or conceptual model to analyse the data by operationalizing them in a coding matrix. An inductive content analysis might use several techniques from grounded theory, such as open and axial coding and constant comparison. However, note that your findings are merely a summary of categories, not a grounded theory.
Analysis software can support you to manage your data, for example by helping to store, annotate and retrieve texts, to locate words, phrases and segments of data, to name and label, to sort and organize, to identify data units, to prepare diagrams and to extract quotes. Still, as a researcher you would do the analytical work by looking at what is in the data, and making decisions about assigning codes, and identifying categories, concepts and patterns. The computer assisted qualitative data analysis (CAQDAS) website provides support to make informed choices between analytical software and courses: http://www.surrey.ac.uk/sociology/research/researchcentres/caqdas/support/choosing . See Box 5 for further reading on qualitative analysis.
Ethnography | • Atkinson P, Coffey A, Delamount S, Lofland J, Lofmand L. Handbook of ethnography. Sage: Thousand Oaks (CA); 2001. • Spradley J. The ethnographic interview. Holt Rinehart & Winston: New York (NY); 1979. • Spradley J. Participant observation. Holt Rinehart & Winston: New York (NY); 1980. |
Phenomenology | • Colaizzi PF. Psychological research as the phenomenologist views it. In: Valle R, King M, editors. Essential phenomenological alternative for psychology. New York (NY): Oxford University Press; 1978. p. 41-78. • Smith J.A, Flowers P, Larkin M. Interpretative phenomenological analysis. Theory, method and research. Sage: London; 2010. |
Grounded theory | • Charmaz K. Constructing grounded theory. 2nd ed. Sage: Thousand Oaks (CA); 2014. • Corbin J, Strauss A. Basics of qualitative research. Techniques and procedures for developing grounded theory. Sage: Los Angeles (CA); 2008. |
Content analysis | • Elo S, Kääriäinen M, Kanste O, Pölkki T, Utriainen K, Kyngäs H. Qualitative Content Analysis: a focus on trustworthiness. Sage Open 2014: 1–10. DOI: 10.1177/2158244014522633. • Elo S. Kyngäs A. The qualitative content analysis process. J Adv Nurs. 2008; 62: 107–115. • Hsieh HF. Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005; 15: 1277–1288. |
The next and final article in this series, Part 4, will focus on trustworthiness and publishing qualitative research [ 13 ].
The authors thank the following junior researchers who have been participating for the last few years in the so-called ‘Think tank on qualitative research’ project, a collaborative project between Zuyd University of Applied Sciences and Maastricht University, for their pertinent questions: Erica Baarends, Jerome van Dongen, Jolanda Friesen-Storms, Steffy Lenzen, Ankie Hoefnagels, Barbara Piskur, Claudia van Putten-Gamel, Wilma Savelberg, Steffy Stans, and Anita Stevens. The authors are grateful to Isabel van Helmond, Joyce Molenaar and Darcy Ummels for proofreading our manuscripts and providing valuable feedback from the ‘novice perspective’.
The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
Published by Alvin Nicolas at August 16th, 2021 , Revised On August 25, 2023
If you are performing research on a large community, organisation, or country, then it may not be possible to collect data individually from each participant. To deal with this issue, you can use a group of a specific number of participants, and this group is referred to as a sample .
The method you apply for selecting your participants is known as the sampling method . It helps in concluding the entire population based on the outcomes of the research .
Example: If you want to research China’s entire population, it isn’t easy to gather information from 1.38 billion people. You can use a sampling method by conducting your research on a specific number of participants and drawing a conclusion about the entire population based on your study’s outcomes.
The sampling method is used to:
Before starting with the sampling methods, it is important to understand the difference between sample and population.
It is a group selected from the target population when you aim to study a large population. This group is considered as the representative of the overall targeted population.
Example: Sample of 20 female cricketers.
If you add the set of individuals with specific characteristics according to the research requirements, the resulting group is called the population.
Example: The income of government teachers in India
Sampling frame.
A list of all the elements from a population is known as the sampling frame.
For instance, you are selecting a telephone directory of students or a list of social media users.
This information can be gathered by contacting any commercial organisation. Sometimes some errors are also possible in the sampling frame due to its discrepancy in selecting samples.
It is considered a subset of the population as it is selected to make the inference to the original population of a study. The chances of accuracy are depended on the size of the population. The larger the size, the more accurate the study is.
When it comes to census, the sample size is the same or parallel with the population size. But to maintain the budget and to consider the time frame, only a representative class is selected.
There are usually two methods of sampling which are used widely. These are considered the best methods:
This method of sampling is conducted by using the method of randomisation. In this method, each individual has an equal and independent opportunity to be selected. It has further sub-categories.
The participants are selected randomly and assigned to the experimental group. It is known as probability sampling. If the selection is not random, it’s considered non-probability sampling.
Example: You want to identify how much time people spend on social media. You need to randomly select the participants and assign a specific number of hours to spend on social media.
Example: You want to find out the benefits of a balanced diet. You need to select the participants randomly and assign a balanced diet.
In this type of sampling, method participants are selected according to the fixed period interval and starting point. The fixed period interval can be calculated by dividing the sample size by the respective population size.
Example: Framingham study , which includes selecting every second person from a list of two residents.
Stratified sampling is a random selection of the participants by dividing them into strata and randomly selecting the participants from each level.
Example: You want to identify how much time people spend on social media. You need to divide the participants into groups based on their age and then assign a specific number of hours to spend on social media.
Example: You want to find out the benefits of a balanced diet. You need to divide participants into various groups based on their age, gender, and health conditions and assigned them to each group’s treatment group.
Even though if participants are selected randomly, they can be assigned to the various groups of comparison. Another procedure for selecting the participants is ‘matching.’ The participants are selected from the controlled group to match the experimental groups’ participants in all aspects based on the dependent variables.
It is a kind of sampling where the population is converted into sub-groups called clusters. These sub-groups or clusters are then selected randomly as a sample. The selected group should have all the characteristics of other groups.
Example: You want to check high school students’ communication skills, and there are more than 50 schools in the city. You can’t visit each school to gather information. In such situations, you can select any five schools, and these schools will be your clusters.
Non-probability sampling techniques are often appropriate for exploratory and qualitative research . This type of sample is not to test a hypothesis about a broad population but to develop an initial understanding of a small or under-researched population.
This type of sampling is different from probability, as its criteria are unique. The samples are not selected randomly; rather, these samples are selected according to the researcher’s ability. This might result in a biased result, and participants may find it difficult to be a part of the sample. Still, this is a prevalent method. It has the following types:
Quota sampling.
Reading material: ResearchProspect has also published a very detailed guide about inductive and deductive reasoning for students.
Purposive Sampling
This type of sampling is based on the aims of the research. Therefore, only such elements of the population will be selected, which are according to the research’s purpose.
Example: You want to find out the opinion of people about jobs and businesses. You can select a few participants interested in doing 9-5 jobs and a few interested in doing business.
This type of sampling is used where the population is not defined or rare. In this technique, one participant is selected according to defined criteria. After that, the same selected participant is asked to refer to other samples fulfilling the study’s criteria. In this way, it goes enlarging its size with the help of the referral.
Example: You can use it while conducting a study on the victims of physical harassment at workplaces. No matter how smoothly you approach them, not all women respond openly to your questions as they feel uncomfortable, or they get afraid of being humiliated. You can select the people from these victims’ circles (ex: their colleagues, friends, relatives) to get in touch with them and gather the required information for your research.
This type of sampling is applied according to availability. If the samples are not available easily, and the research is getting costly, this technique is applied to select the samples as per convenience.
Example: You want to research the election campaigns. In this situation, you need to gather information from the available candidates (political leaders, media persons, voters) whenever and wherever you get any chance to meet them; otherwise, you will need to wait for the next election campaign.
This type of sampling is done when some standards are already adjusted. In this sampling, the representatives are selected from the population. This selected sample should resemble all the characteristics traits of the population. The size of the sample should reflect the. The participants are selected until sufficient information is gathered.
Example: You want to identify and compare the high school’s academic performance, and you are allowed to select only 100 participants as per the standards of your study. You can select 25 students of the ninth standard, 25 students of the tenth standard, 25 students of the eleventh standard, and 25 students of the twelfth standard.
Sampling has many advantages, such as:
What is sampling and its types.
Sampling is the process of selecting a subset of individuals or items from a larger population to gather data. Types include:
Textual analysis is the method of analysing and understanding the text. We need to look carefully at the text to identify the writer’s context and message.
A variable is a characteristic that can change and have more than one value, such as age, height, and weight. But what are the different types of variables?
Quantitative research is associated with measurable numerical data. Qualitative research is where a researcher collects evidence to seek answers to a question.
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Sandeep Kumar
Shevoni Wisidagama
Adv Bukhari
Achini Gunawardena
Morin J.-F., Olsson Ch., Atikcan E. (eds) , Research Methods in the Social Sciences: An A-Z of key concepts, Oxford, OUP
Emilie van Haute
Amandeep Shoker
okedare toyinbliss
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Collecting data using an appropriate sampling technique is a challenging task for a researcher to do. The researchers will be unable to collect data from all possible situations, which will preclude them from answering the study’s research questions in their current form. In light of the enormous number and variety of sampling techniques/methods available, the researcher must be knowledgeable about the differences to select the most appropriate sampling technique/method for the specific study under consideration. In this context, this study also looks into the basic concepts in probability sampling, kinds of probability sampling techniques with their advantages and disadvantages. Social science researchers will benefit from this study since it will assist them in choosing the most suitable probability sampling technique(s) for completing their research smoothly and successfully.
American Journal of Biomedical Science and Research
Kyu-Seong Kim
Mohsin Hassan Alvi
The Manual for Sampling Techniques used in Social Sciences is an effort to describe various types of sampling methodologies that are used in researches of social sciences in an easy and understandable way. Characteristics, benefits, crucial issues/ draw backs, and examples of each sampling type are provided separately.
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Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and irreducible uncertainties. In this paper, the aim is to simplify the computational process while eliminating the dependence on observations. Crucially, the inherent uncertainty in the labels is considered, i.e. the uncertainty of the oracles. Two strategies are proposed, sampling by Klir uncertainty, which tackles the exploration–exploitation dilemma, and sampling by evidential epistemic uncertainty, which extends the concept of reducible uncertainty within the evidential framework, both using the theory of belief functions. Experimental results in active learning demonstrate that our proposed method can outperform uncertainty sampling.
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Data availability.
All data used is this study are available publicly online. The datasets were extracted directly in the repositories available with the links in the folloing section.
The code for theoretical experiments is available at this link: https://anonymous.4open.science/r/evidential-uncertainty-sampling-D453 . Link to the code for the experimental part in active learning: https://anonymous.4open.science/r/evidential-active-learning-B266 .
For details on experiments conducted in theoretical sections, visit: https://anonymous.4open.science/r/evidential-uncertainty-sampling-D453 .
From now on, the model used is K -NN ( K -Nearest Neighbors), with a probabilistic output and on the distance-weighted version available with scikit-learn (Pedregosa et al., 2011 ), every other parameters are scikit-learn default parameters. The uncertainty used is the least confidence measure given in Eq. ( 5 ).
In the example, the word “tails” is written in Finnish, the word “heads” is called Kruuna .
The notion of plausibility within the theory of belief functions used in the proposed methods differs from the one presented here and will be discussed in greater detail in Sect. 4 .
The uncertainty no longer depends on observations, but the model does.
From now, the Evidential K -nearest Neighbors model of (Deœux, 1995 ) is considered.
This representation also applies to labeling performed by a machine.
Experiments where conducted according to the following code: https://anonymous.4open.science/r/evidential-active-learning-B266 .
An entropy of 1 means that the classes are perfectly equidistributed and an entropy of 0 would indicate the total over-representation of one of the classes.
Although it can also be to maximize performance given a cost.
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This work is funded by the Brittany region and the Côtes-d’Armor department. The authors also received funding from IRISA, the University of Rennes, DRUID and Orange SA.
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Arthur Hoarau, Vincent Lemaire, Jean-Christophe Dubois, Yolande Le Gall and Arnaud Martin contributed to the manuscript equally.
Correspondence to Arthur Hoarau .
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Arthur Hoarau, Jean-Christophe Dubois, Yolande Le Gall and Arnaud Martin received research support from the University of Rennes, the IRISA laboratory and the DRUID team. Vincent Lemaire received research support from Orange SA.
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This innovative method utilizes static graphs to represent communication topologies among agents, quantified by a sparsity ratio. In experiments, the focus is on configurations with six agents, examining various degrees of sparsity. The agents, instantiated with models like GPT-3.5 and Mistral 7B, engage in multiple rounds of debate, incorporating responses from their connected peers to refine their answers. For reasoning tasks, datasets such as MATH and GSM8K are used, while alignment labeling tasks employ the Anthropic-HH dataset. The experimental setup includes performance metrics like accuracy and cost savings, and variance reduction techniques are applied to ensure robust results.
The approach using sparse communication topology in MAD achieved notable improvements in both performance and computational efficiency. On the MATH dataset, a neighbor-connected topology improved accuracy by 2% over fully connected MAD while reducing the average input token cost by over 40%. For alignment labeling tasks using the Anthropic-HH dataset, sparse MAD configurations showed improvements in helpfulness and harmlessness metrics by 0.5% and 1.0%, respectively, while halving the computational costs. These results demonstrate that sparse communication topologies can deliver comparable or superior performance to fully connected topologies with significantly reduced computational overhead.
In conclusion, this research presents a significant advancement in the field of AI by introducing sparse communication topology in multi-agent debates. This approach effectively addresses the computational inefficiencies of existing methods, offering a scalable and resource-efficient solution. The experimental results highlight the potential impact of this innovation on AI research, showcasing its ability to enhance performance while reducing costs, thereby advancing the practical applicability of multi-agent systems.
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Sampling methods are crucial for conducting reliable research. In this article, you will learn about the types, techniques and examples of sampling methods, and how to choose the best one for your study. Scribbr also offers free tools and guides for other aspects of academic writing, such as citation, bibliography, and fallacy.
The main methodological issue that influences the generalizability of clinical research findings is the sampling method. In this educational article, we are explaining the different sampling ...
Understand sampling methods in research, from simple random sampling to stratified, systematic, and cluster sampling. Learn how these sampling techniques boost data accuracy and representation, ensuring robust, reliable results. Check this article to learn about the different sampling method techniques, types and examples.
Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research. Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of ...
Abstract. Knowledge of sampling methods is essential to design quality research. Critical questions are provided to help researchers choose a sampling method. This article reviews probability and non-probability sampling methods, lists and defines specific sampling techniques, and provides pros and cons for consideration.
A purposive sampling method was used to select participants who could provide in-depth information about their experiences (Setia, 2017). It is described as the careful selection of a participant ...
Simple random sampling. Simple random sampling involves selecting participants in a completely random fashion, where each participant has an equal chance of being selected.Basically, this sampling method is the equivalent of pulling names out of a hat, except that you can do it digitally.For example, if you had a list of 500 people, you could use a random number generator to draw a list of 50 ...
We could choose a sampling method based on whether we want to account for sampling bias; a random sampling method is often preferred over a non-random method for this reason. Random sampling examples include: simple, systematic, stratified, and cluster sampling. Non-random sampling methods are liable to bias, and common examples include ...
1. Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population. To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.
Evaluate your goals against time and budget. List the two or three most obvious sampling methods that will work for you. Confirm the availability of your resources (researchers, computer time, etc.) Compare each of the possible methods with your goals, accuracy, precision, resource, time, and cost constraints.
Step 1: Define your population. Like other methods of sampling, you must decide upon the population that you are studying. In systematic sampling, you have two choices for data collection: You can select your sample ahead of time from a list and then approach the selected subjects to collect data, or.
The method by which the researcher selects the sample is the ' Sampling Method'. There are essentially two types of sampling methods: 1) probability sampling - based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling - based on researcher's choice, population that accessible & available.
Sampling is a critical element of research design. Different methods can be used for sample selection to ensure that members of the study population reflect both the source and target populations, including probability and non-probability sampling. Power and sample size are used to determine the number of subjects needed to answer the research ...
This paper presents the steps to go through to conduct sampling. Furthermore, as there are different types of sampling techniques/methods, researcher needs to understand the differences to select the proper sampling method for the research. In the regards, this paper also presents the different types of sampling techniques and methods.
Sampling types. There are two major categories of sampling methods ( figure 1 ): 1; probability sampling methods where all subjects in the target population have equal chances to be selected in the sample [ 1, 2] and 2; non-probability sampling methods where the sample population is selected in a non-systematic process that does not guarantee ...
This paper presents the steps to go through to conduct sampling. Furthermore, as there are different types of sampling techniques/methods, researcher needs to understand the differences to select the proper sampling method for the research. In the regards, this paper also presents the different types of sampling techniques and methods.
Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.
This study employed a non-probability sampling approach, as it is commonly used in business research, and its research objectives and inquiries are best addressed through qualitative research ...
SAGE Research Methods. Page 2 of 21. Sampling Strategies in Qualitative Research. 1. 1. Sampling can be divided in a number of different ways. At a basic level, with the exception ... papers on delay in diagnosis, which outline some of the factors tied to delay. So, for example, in rheumatoid arthritis in adults, the central issue was family
Part 2 of the series focused on context, research questions and design of qualitative research . In this paper, Part 3, we address frequently asked questions (FAQs) about sampling, data collection and analysis. ... A sampling plan is a formal plan specifying a sampling method, a sample size, and procedure for recruiting participants (Box 1) . A ...
The method you apply for selecting your participants is known as the sampling method. It helps in concluding the entire population based on the outcomes of the research. Example: If you want to research China's entire population, it isn't easy to gather information from 1.38 billion people.
View PDF. A Manual for Selecting Sampling Techniques in Research. Mohsin Hassan Alvi. The Manual for Sampling Techniques used in Social Sciences is an effort to describe various types of sampling methodologies that are used in researches of social sciences in an easy and understandable way. Characteristics, benefits, crucial issues/ draw backs ...
Corresponding Author. Dana P. Turner MSPH, PhD [email protected] Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
The sample method presented in this paper outperforms previous sampling methods in terms of classification accuracy and distribution similarity with the original data set, as shown in Tables 1 and 2. Furthermore, we can see that the methods for resolving picture similarity based on cosine distance and Euclidean distance have various advantages ...
Current sampling research includes static sampling and adaptive sampling techniques. The former one draws samples based on a preset sample size determined prior to the start of sampling, which encounters the difficulty of determining an appropriate sample size. ... To address the aforementioned issues, in this paper, we devised a sampling ...
Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and irreducible uncertainties. In this paper, the aim is to simplify the computational process while eliminating the dependence on observations. Crucially, the inherent uncertainty in the labels is considered, i.e. the uncertainty of the oracles. Two ...
research, methods must now be proven in more realistic settings. Here we demonstrate for the first time that scalable oversight can help humans more comprehensively assess model-written solutions to real-world assistant tasks. In particular we focus on one of the most important and economically impactful applications of LLM assistants: writing ...
The threshing rate is one of the important indexes to evaluate the effect of corn threshing. The weighing method is often used to calculate the depuration rate of maize at present. This method is time-consuming and laborious and can only calculate the overall threshing rate but does not give the threshing rate of individual corn ears. Different parameters of corn ears have complex effects on ...
Access the portal of NASS, the official source of agricultural data and statistics in the US, and explore various reports and products.
A significant challenge in the realm of large language models (LLMs) is the high computational cost associated with multi-agent debates (MAD). These debates, where multiple agents communicate to enhance reasoning and factual accuracy, often involve a fully connected communication topology. This means each agent references the solutions generated by all other agents, leading to expanded input ...