diff --git a/02_Proposal/proposal.bib b/02_Proposal/proposal.bib
index ec29f34..2fedb25 100644
--- a/02_Proposal/proposal.bib
+++ b/02_Proposal/proposal.bib
@@ -238,6 +238,16 @@
   volume = {141}
 }
 
+@inproceedings{cosley2003seeing,
+  author = {Cosley, Dan and Lam, Shyong K and Albert, Istvan and Konstan, Joseph A and Riedl, John},
+  booktitle = {Proceedings of the {{SIGCHI}} Conference on {{Human}} Factors in Computing Systems},
+  date = {2003},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\X5WCB36N\\Cosley et al_2003_Is seeing believing.pdf},
+  organization = {{ACM}},
+  pages = {585-592},
+  title = {Is Seeing Believing?: How Recommender System Interfaces Affect Users' Opinions}
+}
+
 @article{crottGroupDecisionChoice1991,
   author = {Crott, Helmut W and Szilvas, Klaus and Zuber, Johannes A},
   date = {1991-06},
@@ -309,6 +319,23 @@
   volume = {73}
 }
 
+@incollection{felfernigBiasesGroupDecisions2018,
+  abstract = {Decision biases can be interpreted as tendencies to think and act in specific ways that result in a systematic deviation of potentially rational and high-quality decisions. In this chapter, we provide an overview of example decision biases and show possibilities to counteract these. The overview includes (1) biases that exist in both single user and group decision making (decoy effects, serial position effects, framing, and anchoring) and (2) biases that especially occur in the context of group decision making (GroupThink, polarization, and emotional contagion).},
+  author = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal{\v c}i{\v c}, Marko},
+  booktitle = {Group {{Recommender Systems}} : {{An Introduction}}},
+  date = {2018},
+  doi = {10.1007/978-3-319-75067-5_8},
+  editor = {Felfernig, Alexander and Boratto, Ludovico and Stettinger, Martin and Tkal{\v c}i{\v c}, Marko},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\NPY64XZ8\\Felfernig et al_2018_Biases in Group Decisions.pdf},
+  isbn = {978-3-319-75067-5},
+  langid = {english},
+  location = {{Cham}},
+  pages = {145-155},
+  publisher = {{Springer International Publishing}},
+  series = {{{SpringerBriefs}} in {{Electrical}} and {{Computer Engineering}}},
+  title = {Biases in {{Group Decisions}}}
+}
+
 @inproceedings{felfernigConstraintbasedRecommenderSystems2008,
   abstract = {Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.},
   author = {Felfernig, A and Burke, R},
@@ -422,6 +449,26 @@
   title = {Persuasive {{Recommendation}}: {{Serial Position Effects}} in {{Knowledge}}-{{Based Recommender Systems}}}
 }
 
+@inproceedings{felfernigPersuasiveRecommendationSerial2007a,
+  abstract = {Recommender technologies are crucial for the effective support of customers in online sales situations. The state-of-the-art research in recommender systems is not aware of existing theories in the areas of cognitive and decision psychology and thus lacks of deeper understanding of online buying situations. In this paper we present results from user studies related to serial position effects in human memory in the context of knowledge-based recommender applications. We discuss serial position effects on the recall of product descriptions as well as on the probability of product selection. Serial position effects such as primacy and recency are major building blocks of persuasive, next generation knowledge-based recommender systems.},
+  author = {Felfernig, A. and Friedrich, G. and Gula, B. and Hitz, M. and Kruggel, T. and Leitner, G. and Melcher, R. and Riepan, D. and Strauss, S. and Teppan, E. and Vitouch, O.},
+  booktitle = {Persuasive {{Technology}}},
+  date = {2007},
+  doi = {10.1007/978-3-540-77006-0_34},
+  editor = {de Kort, Yvonne and IJsselsteijn, Wijnand and Midden, Cees and Eggen, Berry and Fogg, B. J.},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\FK4JBI59\\Felfernig et al_2007_Persuasive Recommendation.pdf},
+  isbn = {978-3-540-77006-0},
+  keywords = {human memory,interactive selling,knowledge-based recommendation,persuasive technologies,recommender systems},
+  langid = {english},
+  location = {{Berlin, Heidelberg}},
+  options = {useprefix=true},
+  pages = {283-294},
+  publisher = {{Springer}},
+  series = {Lecture {{Notes}} in {{Computer Science}}},
+  shorttitle = {Persuasive {{Recommendation}}},
+  title = {Persuasive {{Recommendation}}: {{Serial Position Effects}} in {{Knowledge}}-{{Based Recommender Systems}}}
+}
+
 @inproceedings{felfernigProceedings20thInternational,
   author = {Felfernig, Alexander and Tiihonen, Juha and Hotz, Lothar and Stettinger, Martin},
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\FW39YC58\\Felfernig et al. - University of Hamburg Hamburger Informatik Technol.pdf},
@@ -568,6 +615,23 @@
   title = {Recommendation to {{Groups}}}
 }
 
+@book{janis1982groupthink,
+  author = {Janis, Irving Lester and Janis, Irving Lester},
+  date = {1982},
+  publisher = {{Houghton Mifflin Boston}},
+  title = {Groupthink: {{Psychological}} Studies of Policy Decisions and Fiascoes},
+  volume = {349}
+}
+
+@incollection{janisGroupthink1991,
+  author = {Janis, Irving},
+  booktitle = {A {{First Look}} at {{Communication Theory}}},
+  date = {1991},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\3JN8DV3U\\griffin-groupthink-challenger.pdf},
+  pages = {235-246},
+  title = {Groupthink}
+}
+
 @article{kerrBiasJudgmentComparing1996,
   author = {Kerr, Norbert L and MacCoun, Robert J and Kramer, Geoffrey P},
   date = {1996},
@@ -665,6 +729,21 @@
   volume = {108}
 }
 
+@article{murphyPrimacyRecencyEffects2006,
+  author = {Murphy, Jamie and Hofacker, Charles and Mizerski, Richard},
+  date = {2006-01},
+  doi = {10.1111/j.1083-6101.2006.00025.x},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\29P4CLMW\\Murphy et al. - 2006 - Primacy and Recency Effects on Clicking Behavior.pdf},
+  issn = {1083-6101, 1083-6101},
+  journaltitle = {Journal of Computer-Mediated Communication},
+  langid = {english},
+  number = {2},
+  pages = {522-535},
+  shortjournal = {J Comp Mediated Comm},
+  title = {Primacy and {{Recency Effects}} on {{Clicking Behavior}}},
+  volume = {11}
+}
+
 @inproceedings{ninausINTELLIREQIntelligentTechniques2014,
   abstract = {Requirements Engineering is considered as one of the most critical phases of a software development project. Low-quality requirements are a major reason for the failure of a project. Consequently, techniques are needed that help to improve the support of stakeholders in the development of requirements models as well as in the process of deciding about the corresponding release plans. In this paper we introduce the INTELLIREQ Requirements Engineering environment. This environment is based on different recommendation approaches that support stakeholders in requirements-related activities such as definition, quality assurance, reuse, and release planning. We provide an overview of recommendation approaches integrated in INTELLIREQ and report results of empirical studies that show in which way recommenders can improve the quality of Requirements Engineering processes.},
   author = {Ninaus, Gerald and Felfernig, Alexander and Stettinger, Martin and Reiterer, Stefan and Leitner, Gerhard and Weninger, Leopold and Schanil, Walter},
@@ -842,6 +921,24 @@ OCLC: 935904837},
   volume = {52}
 }
 
+@inproceedings{stettingerCounteractingAnchoringEffects2015,
+  abstract = {Similar to single user decisions, group decisions can be affected by decision biases. In this paper we analyze anchoring effects as a specific type of decision bias in the context of group decision scenarios. On the basis of the results of a user study in the domain of software requirements prioritization we discuss results regarding the optimal time when preference information of other users should be disclosed to the current user. Furthermore, we show that explanations can increase the satisfaction of group members with various aspects of a group decision process (e.g., satisfaction with the decision and decision support quality).},
+  author = {Stettinger, Martin and Felfernig, Alexander and Leitner, Gerhard and Reiterer, Stefan},
+  booktitle = {User {{Modeling}}, {{Adaptation}} and {{Personalization}}},
+  date = {2015},
+  doi = {10.1007/978-3-319-20267-9_10},
+  editor = {Ricci, Francesco and Bontcheva, Kalina and Conlan, Owen and Lawless, S\'eamus},
+  file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\WEJED597\\Stettinger et al_2015_Counteracting Anchoring Effects in Group Decision Making.pdf},
+  isbn = {978-3-319-20267-9},
+  keywords = {Anchoring effects,Decision biases,Group decision making,Recommender systems},
+  langid = {english},
+  location = {{Cham}},
+  pages = {118-130},
+  publisher = {{Springer International Publishing}},
+  series = {Lecture {{Notes}} in {{Computer Science}}},
+  title = {Counteracting {{Anchoring Effects}} in {{Group Decision Making}}}
+}
+
 @misc{StudienUndPrufungsordnung2015,
   date = {2015-09-29},
   file = {C\:\\Users\\Hannes.Kuchelmeister\\Zotero\\storage\\8TDXS8ES\\2015 - Studien- und Prüfungsordnung des Karlsruher Instit.pdf},
diff --git a/02_Proposal/sections/10_foundations.tex b/02_Proposal/sections/10_foundations.tex
index 45f553e..d3b75b1 100644
--- a/02_Proposal/sections/10_foundations.tex
+++ b/02_Proposal/sections/10_foundations.tex
@@ -60,11 +60,23 @@ with preferences of user \( i \) as \( PREF_i \) \cite{ felferningGroupBasedConf
 \]
 and where \( C_{CONF} \cup C_{PREF} \cup C_{KB} \) is consistent \cite{ felferningGroupBasedConfiguration2016}.
 
-\todo[inline]{add group descision part, conflict resolution, types of conflict, differences in knowledge/decision power;
+\section{Group Bias}
+\label{sec:Foundations:GroupBias}
 
-group dynamics (e.g. biases in decision making, anchoring, etc.)
+Groups have biases same as individuals. Some of these stem from individual biases that are transferred to a group and others only occur in group settings. This section covers some of those effects. These effects are described by \citeauthor{felfernigBiasesGroupDecisions2018} \cite{felfernigBiasesGroupDecisions2018}.
 
-Why are group decision interesting?}
+\subsection{GroupThink}
+
+The bias called GroupThink occurs, when group members prefer to avoid conflicts instead of being mainly interested in getting the best decision outcome. Alternative options are in these circumstances not analysed close enough \cite{janis1982groupthink}.
+An avoidance strategy for this effect is for leaders to delay stating there opinion until all alternatives have been viewed in detail \citeauthor{felfernigBiasesGroupDecisions2018}. Recommender systems can aid group decision quality through encouraged information exchange as discussed by \citeauthor{atasItemRecommendationUsing2017} in \cite{atasItemRecommendationUsing2017}.
 
 
+\subsection{Anchoring}
+
+The first information provided is relied upon to heavily. In a group setting this can be triggered by the group member to first express their opinion. Often this is triggered if group members see preferences of others too early, therefore recommender systems should not show the rating of other users \cite{cosley2003seeing}.
+
+\subsection{Serial Position Effects}
+
+The serial positioning effects is the effect that users have a higher retention rate of items in a list that are presented first or last and these items are more closely examined \cite{felfernigPersuasiveRecommendationSerial2007,murphyPrimacyRecencyEffects2006}. Additionally to remembering and examining items at the start and the end of a list also the order of items can change the option the user chooses in the end \cite{felfernigBiasesGroupDecisions2018}.
+